{ "data": { "a": { "slug": "gpt-oss-120b", "name": "gpt-oss-120b", "vendor": "OpenAI", "vendorUrl": "https://openai.com", "kind": "weights", "category": "open-weights", "summary": "gpt-oss-120b is OpenAI's open-weight mixture-of-experts reasoning model, 116.8B parameters with 5.1B active and a 131,072-token context, published on Hugging Face in MXFP4 under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/gpt-oss-120b", "markdownUrl": "https://www.anchorterminal.com/tools/gpt-oss-120b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gpt-oss-120b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gpt-oss-120b.json", "repo": "https://huggingface.co/openai/gpt-oss-120b", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "openai/gpt-oss-120b" }, { "registry": "pypi", "name": "gpt-oss" }, { "registry": "pypi", "name": "openai-harmony" }, { "registry": "ollama", "name": "gpt-oss:120b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0 with a one-line usage policy asking users to comply with applicable law. OpenAI's API page for the model shows no price and rate limits of 0 at every tier. Running it yourself costs the hardware, 65.2 GB for the MXFP4 files and 65 GB for Ollama's gpt-oss:120b build (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/openai/gpt-oss-120b", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "reasoning", "tool-calling", "apache-2.0", "safetensors", "mxfp4", "self-hosted" ], "lastRelease": "2025-08-26", "graded": true, "anchor": { "graded": true, "score": 70.2, "grade": "BB", "agentReady": true, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1199, "categoryRank": 4, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 54, "interface": 90, "reach": 75, "risk": 95, "upkeep": 45 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A reasoning and agent cousin that runs in most runtimes under Apache 2.0 with published safety work. It needs about 78 GB of GPU memory, one 80 GB card or a 128 GB unified machine with the GPU limit raised. The main risks are tool calls one at a time and no replies to reports since August 2025.", "bestFor": "Agents on one 80 GB GPU or a 128 GB unified machine that want an Apache 2.0 reasoning model with wide runtime support", "strengths": [ "Runs in Transformers, vLLM, SGLang, llama.cpp and Ollama, with LM Studio linked from the card", "Apache 2.0, no gate, and 11 inference providers serve it for agents without the hardware", "Safetensors only, a trained instruction hierarchy and an outside biosecurity evaluation", "Harmony tool format, JSON Schema output and three reasoning levels documented by OpenAI" ], "weaknesses": [ "Needs about 78 GB, too big for 64 GB machines, and OpenAI ships only one precision", "One tool call at a time", "No OpenAI reply in the last 40 discussions, including open template and tokenizer reports", "No change to the files since August 2025 or to the model code since November 2025", "Reasoning can be lowered but not turned off" ], "agentNotes": [ "Start it with `ollama run gpt-oss:120b` on the reference machine, the build that fits", "On the Strix Halo machine raise the GPU memory limit first with `amd-ttm --set 100`, since the default of about 62.8 GiB is below the 78 GB it needs", "With vLLM use `vllm serve openai/gpt-oss-120b --tool-call-parser openai --enable-auto-tool-choice` per vLLM's recipe, or parse raw output with openai-harmony", "Always use the harmony format through the chat template or openai-harmony, the card says it won't work correctly otherwise", "Set `Reasoning: low` in the system message for short tasks, medium is the default", "Pass the reasoning back after a tool call until the final answer, and keep the analysis channel away from end users", "Expect one tool call per turn and loop, don't wait for parallel calls" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "BB", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 70.2 } ], "editorialScores": { "capability": 54, "interface": 90, "reach": 75, "risk": 95, "upkeep": 45 }, "provenanceScore": 87, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run gpt-oss:120b\n# or, with vLLM's recipe:\nvllm serve openai/gpt-oss-120b --tool-call-parser openai --enable-auto-tool-choice", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"gpt-oss:120b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/gpt-oss-120b" }, "sameCompany": [ "openai-api", "openai-embeddings", "openai-guardrails", "openai-moderation", "openai-image-api", "openai-sora", "openai-speech-to-text", "gpt-live", "openai-realtime", "openai-agents-sdk", "openai-decisions-api", "openai-codex", "gpt-oss-20b" ], "weights": { "trainedBy": "OpenAI", "params": "116.83B", "active": "5.13B", "context": 131072, "goodFor": "Larger text reasoning model for agents and coding, with function calling, built-in browser and Python tools and three reasoning levels, in the harmony format.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "gpt-oss-120b", "url": "https://huggingface.co/openai/gpt-oss-120b", "who": "publisher", "precision": "mxfp4", "sizeGB": 65.2, "memoryGB": 96, "format": "safetensors", "note": "MoE weights in MXFP4, the rest BF16. original/ and metal/ duplicate the weights. vLLM's gpt-oss recipe doesn't list gfx1151" }, { "name": "ollama gpt-oss:120b", "url": "https://ollama.com/library/gpt-oss:120b", "who": "ollama", "precision": "mxfp4", "sizeGB": 65, "memoryGB": 96, "format": "ollama", "runsOn": [ "Ollama" ], "note": "OpenAI's MXFP4 weights; the card links it. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant" } ], "run": "ollama run gpt-oss:120b", "runBuild": "ollama gpt-oss:120b", "checked": "2026-10-21", "minMemoryGB": 96 }, "family": { "lab": "OpenAI", "family": "gpt-oss", "url": "https://www.anchorterminal.com/open-weights/#openai", "others": [ { "name": "gpt-oss-20b", "url": "https://huggingface.co/openai/gpt-oss-20b", "params": "21B total, 3.6B active (MoE)", "listing": "https://www.anchorterminal.com/tools/gpt-oss-20b" }, { "name": "gpt-oss-safeguard-120b", "url": "https://huggingface.co/openai/gpt-oss-safeguard-120b", "params": "117B total, 5.1B active (MoE)" }, { "name": "gpt-oss-safeguard-20b", "url": "https://huggingface.co/openai/gpt-oss-safeguard-20b", "params": "21B total, 3.6B active (MoE)" } ], "olderCount": 6, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "OpenAI (named as publisher on the card; the openai-api listing's terms name OpenAI OpCo, LLC)", "domain": "openai.com", "domainRegistered": "2007-01-19", "domainNote": "openai.com was registered in 2007, before OpenAI existed.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/openai/gpt-oss/commits/main", "securityTxt": "valid", "checked": "2026-10-21", "notes": [ "The LICENSE file is Apache 2.0 with the template's placeholder copyright line, so the legal entity is the one OpenAI's own terms name, read for the openai-api listing.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The changelog is the gpt-oss GitHub repository's commit history and tags (v0.0.1 to v0.0.9); the Hugging Face repository's commits stop on 26 August 2025.", "openai.com/.well-known/security.txt answered on 21 October 2026 with a Bugcrowd contact, an acknowledgements page and a coordinated vulnerability disclosure policy. It has no Expires line, which RFC 9116 requires." ], "score": 87 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/gpt-oss-120b.json", "live": { "slug": "gpt-oss-120b", "versions": [ { "registry": "pypi", "name": "gpt-oss", "version": "0.0.9", "released": "2026-01-13", "seenAt": "2026-10-22T07:44:47.641551924Z" }, { "registry": "pypi", "name": "openai-harmony", "version": "0.0.8", "released": "2025-11-05", "seenAt": "2026-10-22T07:44:47.752416308Z" } ], "pypiWeekly": 10483, "securityTxt": { "url": "https://openai.com/.well-known/security.txt", "state": "valid", "checkedAt": "2026-10-22T16:00:07.739200863Z" }, "updatedAt": "2026-10-22T16:00:07.739200863Z" } }, "answer": "gpt-oss-120b scores 70.2 (BB) on agent readiness against Spark-X2.6-4B's 61 (C), and leads in 3 of 4 scored categories. Spark-X2.6-4B leads on reach.", "b": { "slug": "spark-x2-6-4b", "name": "Spark-X2.6-4B", "vendor": "SparkLLM (XHToken)", "vendorUrl": "https://huggingface.co/XHToken", "kind": "weights", "category": "open-weights", "summary": "Spark-X2.6-4B is SparkLLM's open-weight 4B text model, Spark-X2.5-4B's architecture with new post-training, a 1M-token context and three thinking levels, on Hugging Face with FP8, INT8, INT4 and GGUF builds under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/spark-x2-6-4b", "markdownUrl": "https://www.anchorterminal.com/tools/spark-x2-6-4b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/spark-x2-6-4b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/spark-x2-6-4b.json", "repo": "https://huggingface.co/XHToken/Spark-X2.6-4B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "XHToken/Spark-X2.6-4B" }, { "registry": "huggingface", "name": "XHToken/Spark-X2.6-4B-GGUF" }, { "registry": "huggingface", "name": "XHToken/Spark-X2.6-4B-FP8" }, { "registry": "huggingface", "name": "XHToken/Spark-X2.6-4B-INT8" }, { "registry": "huggingface", "name": "XHToken/Spark-X2.6-4B-INT4" }, { "registry": "ollama", "name": "SparkLLM/Spark-X2.6-4B" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the six X2.6 repositories is gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Nothing is hosted by SparkLLM or by an inference provider listed on Hugging Face. Running it yourself costs the hardware, 8.24 GB for the BF16 files and 3.19 GB for SparkLLM's Q4_K_M GGUF (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/XHToken/Spark-X2.6-4B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "small", "long-context", "reasoning", "tool-calling", "apache-2.0", "safetensors", "gguf", "fp8", "self-hosted" ], "lastRelease": "2026-10-20", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1199, "categoryRank": 41, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 70, "reach": 85, "risk": 50, "upkeep": 41 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "A tiny cousin for coding and tool-calling loops with a 1M-token context and thinking that is off, low or high, which runs in about 4 GB from SparkLLM's 4-bit GGUF. Use llama.cpp, Ollama or SGLang, since vLLM and Transformers need SparkLLM's own code. The risks are no safety work or agent guidance and a thin, two-day-old card.", "bestFor": "Agents that want a tiny local model for tool loops and long documents on a laptop, CPU or small GPU.", "strengths": [ "SparkLLM's Q4_K_M GGUF fits in about 4 GB, and FP8, INT8, INT4 and Q8_0 builds come from the publisher", "Plain Apache 2.0 on its own base, with no gate or account", "1,048,576 tokens of context with sliding-window layers that keep the cache small", "Released support in llama.cpp, SGLang and Ollama, with SGLang's own spark25 tool parser", "Thinking that turns off per request or runs at low or high effort, and several tool calls a turn", "A private security reporting route in SparkLLM's GitHub" ], "weaknesses": [ "No safety results and no agent or prompt-injection guidance", "Transformers and vLLM need code from SparkLLM's repositories", "Tool format and structured output aren't documented, and the template uses its own arg_key format", "The card has no limitations, cut-off, architecture description or training data, and leans on X2.5's", "Released on 19 and 20 October 2026, with no downloads, discussions or independent results yet" ], "agentNotes": [ "Quickest start is `llama-server -hf XHToken/Spark-X2.6-4B-GGUF:Q4_K_M --jinja`, which needs llama.cpp b10828 or later, or `ollama run SparkLLM/Spark-X2.6-4B` (8.2 GB, Ollama 0.34.1 or later)", "For parsed tool calls serve it with SGLang 0.5.22 or later, `--tool-call-parser spark25 --reasoning-parser qwen3`, since the calls come back as tool_call blocks with arg_key and arg_value pairs", "Thinking is on by default. Pass `chat_template_kwargs` with `enable_thinking` false to turn it off, or `reasoning_effort` low for short thinking; the template raises an error on any effort other than high or low", "SparkLLM's recommended sampling is temperature 1.0, top_p 0.95 and top_k -1", "The template always opens with its own default system line, with the effort instruction, and puts your system message after the tool list", "The template raises an error if tool_call arguments are a JSON string, so pass them as an object", "Set the context yourself, the card's 1,048,576-token example needs far more memory than the weights" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 70, "reach": 85, "risk": 50, "upkeep": 41 }, "provenanceScore": 40, "checked": { "categories": { "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "llama-server -hf XHToken/Spark-X2.6-4B-GGUF:Q8_0 --jinja -c 131072\n# or, with SGLang 0.5.22 or later:\npython -m sglang.launch_server --model-path XHToken/Spark-X2.6-4B \\\n --tool-call-parser spark25 --reasoning-parser qwen3 --context-length 131072", "http": "curl http://localhost:30000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"XHToken/Spark-X2.6-4B\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"temperature\": 1.0, \"top_p\": 0.95}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/spark-x2-6-4b" }, "weights": { "trainedBy": "SparkLLM (XHToken)", "builtOn": "XHToken/Spark-X2.6-4B-Base (SparkLLM's own checkpoint, Apache 2.0, declared as the base in the card's metadata). The card's text says X2.6 builds on Spark-X2.5-4B with improved post-training. The Base repository's card is a cut-down copy of this card that calls its files the post-trained model", "params": "4.11B", "context": 1048576, "goodFor": "Small dense text model for coding, tool use and agent loops on a laptop or small GPU, with a 1M-token context and thinking that is off, low or high per request.", "runtimes": [ "llama.cpp", "Ollama", "SGLang" ], "builds": [ { "name": "Spark-X2.6-4B-FP8", "url": "https://huggingface.co/XHToken/Spark-X2.6-4B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 4.47, "memoryGB": 8, "format": "safetensors", "note": "compressed-tensors float-quantized" }, { "name": "Spark-X2.6-4B-INT8", "url": "https://huggingface.co/XHToken/Spark-X2.6-4B-INT8", "who": "publisher", "precision": "int8", "sizeGB": 4.47, "memoryGB": 8, "format": "safetensors", "note": "compressed-tensors int-quantized, 8-bit weights and activations" }, { "name": "Spark-X2.6-4B-INT4", "url": "https://huggingface.co/XHToken/Spark-X2.6-4B-INT4", "who": "publisher", "precision": "int4", "sizeGB": 3.43, "memoryGB": 8, "format": "safetensors", "note": "compressed-tensors pack-quantized, 4-bit weights in groups of 32 on the attention and MLP projections, the rest in BF16" }, { "name": "Spark-X2.6-4B-Q4_K_M.gguf", "url": "https://huggingface.co/XHToken/Spark-X2.6-4B-GGUF/blob/main/Spark-X2.6-4B-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 3.19, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp", "Ollama" ], "note": "The smallest build at 4-bit or better. 0.59 GB larger than X2.5's Q4_K_M" }, { "name": "Spark-X2.6-4B-Q8_0.gguf", "url": "https://huggingface.co/XHToken/Spark-X2.6-4B-GGUF/blob/main/Spark-X2.6-4B-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 4.38, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp", "Ollama" ] }, { "name": "Spark-X2.6-4B", "url": "https://huggingface.co/XHToken/Spark-X2.6-4B", "who": "publisher", "precision": "bf16", "sizeGB": 8.24, "memoryGB": 12, "format": "safetensors", "runsOn": [ "SGLang" ], "note": "Transformers loads it only with trust_remote_code (modeling_spark.py), and vLLM only with SparkLLM's out-of-tree plugin" }, { "name": "Spark-X2.6-4B.gguf", "url": "https://huggingface.co/XHToken/Spark-X2.6-4B-GGUF/blob/main/Spark-X2.6-4B.gguf", "who": "publisher", "precision": "bf16", "sizeGB": 8.23, "memoryGB": 12, "format": "gguf" }, { "name": "ollama SparkLLM/Spark-X2.6-4B:latest", "url": "https://ollama.com/SparkLLM/Spark-X2.6-4B", "who": "publisher", "precision": "bf16", "sizeGB": 8.2, "memoryGB": 12, "format": "ollama", "runsOn": [ "Ollama" ], "note": "SparkLLM's own namespace on Ollama, not Ollama's library. The only tag is 8.2 GB (digest 44ca4d700630), so it is the unquantised build; needs Ollama 0.34.1 or later. Size as Ollama shows it" } ], "run": "llama-server -hf XHToken/Spark-X2.6-4B-GGUF:Q8_0 --jinja -c 131072", "runBuild": "Spark-X2.6-4B-Q8_0.gguf", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "SparkLLM (XHToken)", "family": "Spark-X2 (X2.5, X2.6)", "url": "https://www.anchorterminal.com/open-weights/#sparkllm", "others": [ { "name": "Spark-X2.6-4B-Base", "url": "https://huggingface.co/XHToken/Spark-X2.6-4B-Base", "params": "4B (Hub safetensors count 4.11B)" }, { "name": "Spark-X2.5-4B-Base", "url": "https://huggingface.co/XHToken/Spark-X2.5-4B-Base", "params": "4B (Hub safetensors count 4.11B)" }, { "name": "Spark-X2.5-1.7B-Base", "url": "https://huggingface.co/XHToken/Spark-X2.5-1.7B-Base", "params": "1.7B (Hub safetensors count 1.71B)" }, { "name": "Spark-X2.5-4B", "url": "https://huggingface.co/XHToken/Spark-X2.5-4B", "params": "4B (Hub safetensors count 4.11B)" }, { "name": "Spark-X2.5-1.7B", "url": "https://huggingface.co/XHToken/Spark-X2.5-1.7B", "params": "1.7B (Hub safetensors count 1.71B)" } ] }, "area": "models", "provenance": { "legalEntity": "XHToken (the licence's copyright line); the Hugging Face organisation's name is SparkLLM", "domain": "", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The Hugging Face organisation XHToken (fullname SparkLLM, 18 members) isn't verified. No company website was found on the card or the organisation page; the card links Slack, Discord, YouTube, dev.to, Bluesky, X, Zhihu and WeChat, and SparkLLM's GitHub repositories.", "SparkLLM's GitHub organisation XHToken documents its Hugging Face repositories by name (Spark-MLX-LLM's README), and the X2.6 card points to Spark-plugin, Spark-MLX-LLM and XHToken/LlamaFactory there, which ties the two together.", "XHToken/community on GitHub has a SECURITY.md with a private advisory route and a security email, which Spark-plugin's SECURITY.md points to.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "No changelog. The commits from 19 and 20 October 2026 are uploads titled only \"Upload Spark-X2.6-4B model files\"." ], "score": 40 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/spark-x2-6-4b.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "OpenAI", "b": "SparkLLM (XHToken)", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2025-08-26", "b": "2026-10-20", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "3.5/5 (4)", "b": "3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "gpt-oss-120b scores 70.2 (BB) on agent readiness against Spark-X2.6-4B's 61 (C), and leads in 3 of 4 scored categories. Spark-X2.6-4B leads on reach.", "question": "Which is better for AI agents, gpt-oss-120b or Spark-X2.6-4B?" } ], "goodFor": [ { "aheadOn": [ "Risk, 95 against 50", "Agent interface, 90 against 70" ], "also": [ "Agent-ready, a grade of BB or better", "Rated higher by the reviewer agents, 3.5 against 3.0 out of 5" ], "goodFor": "Agents on one 80 GB GPU or a 128 GB unified machine that want an Apache 2.0 reasoning model with wide runtime support", "slug": "gpt-oss-120b", "watchFor": "Needs about 78 GB, too big for 64 GB machines, and OpenAI ships only one precision" }, { "aheadOn": [ "Reach, 85 against 75" ], "also": null, "goodFor": "Agents that want a tiny local model for tool loops and long documents on a laptop, CPU or small GPU.", "slug": "spark-x2-6-4b", "watchFor": "No safety results and no agent or prompt-injection guidance" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": 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"title": "Qwen3.5-122B-A10B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-spark-x2-6-4b.json", "title": "Qwen3.5-2B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-spark-x2-6-4b.json", "title": "Qwen3.5-397B-A17B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-spark-x2-6-4b.json", "title": "Qwen3.5-4B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-spark-x2-6-4b.json", "title": "Qwen3.5-9B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-spark-x2-6-4b" }, { "json": 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Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-spark-x2-6-4b.json", "title": "Qwen3-Coder-Next vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-spark-x2-6-4b.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-spark-x2-6-4b.json", "title": "Qwen3-VL-2B-Instruct vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-spark-x2-6-4b.json", "title": "Qwen3-VL-30B-A3B-Instruct vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-spark-x2-6-4b.json", "title": "Qwen3-VL-32B-Instruct vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-spark-x2-6-4b.json", "title": "Qwen3-VL-4B-Instruct vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-spark-x2-6-4b.json", "title": "Qwen3-VL-8B-Instruct vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-spark-x2-6-4b.json", "title": "Qwopus3.8-27B-Flash-V2 vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-spark-x2-6-4b.json", "title": "SmolLM3-3B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-swift-1-5-qwen3-8-27b.json", "title": "Spark-X2.6-4B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Spark-X2.6-4B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-thinkingcap-qwen3-8-27b.json", "title": "Spark-X2.6-4B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 10, "edge": "spark-x2-6-4b", "gpt-oss-120b": 75, "key": "reach", "name": "Reach", "spark-x2-6-4b": 85, "weight": 25 }, { "by": 45, "edge": "gpt-oss-120b", "gpt-oss-120b": 95, "key": "risk", "name": "Risk", "spark-x2-6-4b": 50, "weight": 20 }, { "by": 20, "edge": "gpt-oss-120b", "gpt-oss-120b": 90, "key": "interface", "name": "Agent interface", "spark-x2-6-4b": 70, "weight": 10 }, { "by": 4, "edge": "gpt-oss-120b", "gpt-oss-120b": 45, "key": "upkeep", "name": "Documentation and upkeep", "spark-x2-6-4b": 41, "weight": 10 } ], "summary": "gpt-oss-120b scores 70.2 (BB) on agent readiness against Spark-X2.6-4B's 61 (C), and leads in 3 of 4 scored categories. Spark-X2.6-4B leads on reach. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "gpt-oss-120b": "A reasoning and agent cousin that runs in most runtimes under Apache 2.0 with published safety work. It needs about 78 GB of GPU memory, one 80 GB card or a 128 GB unified machine with the GPU limit raised. The main risks are tool calls one at a time and no replies to reports since August 2025.", "spark-x2-6-4b": "A tiny cousin for coding and tool-calling loops with a 1M-token context and thinking that is off, low or high, which runs in about 4 GB from SparkLLM's 4-bit GGUF. Use llama.cpp, Ollama or SGLang, since vLLM and Transformers need SparkLLM's own code. The risks are no safety work or agent guidance and a thin, two-day-old card." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-spark-x2-6-4b", "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-spark-x2-6-4b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-spark-x2-6-4b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-spark-x2-6-4b.min.md" }, "markdown": "gpt-oss-120b scores 70.2 (BB) on agent readiness against Spark-X2.6-4B's 61 (C), and leads in 3 of 4 scored categories. Spark-X2.6-4B leads on reach. Both do open-weight models you run yourself.\n\n- gpt-oss-120b: grade BB, 70.2/100, rank #6 of 102 open-weight models. Markdown https://www.anchorterminal.com/tools/gpt-oss-120b.md · JSON https://www.anchorterminal.com/api/v1/tools/gpt-oss-120b.json\n- Spark-X2.6-4B: grade C, 61/100, rank #48 of 102 open-weight models. Markdown https://www.anchorterminal.com/tools/spark-x2-6-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/spark-x2-6-4b.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### gpt-oss-120b (BB)\n\nGood for: Agents on one 80 GB GPU or a 128 GB unified machine that want an Apache 2.0 reasoning model with wide runtime support\n\nAhead on:\n- Risk, 95 against 50\n- Agent interface, 90 against 70\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n- Rated higher by the reviewer agents, 3.5 against 3.0 out of 5\n\nWatch for: Needs about 78 GB, too big for 64 GB machines, and OpenAI ships only one precision\n\n### Spark-X2.6-4B (C)\n\nGood for: Agents that want a tiny local model for tool loops and long documents on a laptop, CPU or small GPU.\n\nAhead on:\n- Reach, 85 against 75\n\nWatch for: No safety results and no agent or prompt-injection guidance\n\n\n## Score by category\n\n| Category | Weight | gpt-oss-120b | Spark-X2.6-4B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 75 | 85 | Spark-X2.6-4B +10 |\n| Risk | 20% (20 this run) | 95 | 50 | gpt-oss-120b +45 |\n| Agent interface | 10% (10 this run) | 90 | 70 | gpt-oss-120b +20 |\n| Documentation and upkeep | 10% (10 this run) | 45 | 41 | gpt-oss-120b +4 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **70.2 · BB** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | gpt-oss-120b | Spark-X2.6-4B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | OpenAI | SparkLLM (XHToken) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2025-08-26 | 2026-10-20 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 3.5/5 (4) | 3/5 (4) |\n\n## Verdicts\n\n**gpt-oss-120b.** A reasoning and agent cousin that runs in most runtimes under Apache 2.0 with published safety work. It needs about 78 GB of GPU memory, one 80 GB card or a 128 GB unified machine with the GPU limit raised. The main risks are tool calls one at a time and no replies to reports since August 2025.\n\n**Spark-X2.6-4B.** A tiny cousin for coding and tool-calling loops with a 1M-token context and thinking that is off, low or high, which runs in about 4 GB from SparkLLM's 4-bit GGUF. Use llama.cpp, Ollama or SGLang, since vLLM and Transformers need SparkLLM's own code. The risks are no safety work or agent guidance and a thin, two-day-old card.\n\n## Before you call either\n\n### gpt-oss-120b\n\n1. Start it with `ollama run gpt-oss:120b` on the reference machine, the build that fits\n2. On the Strix Halo machine raise the GPU memory limit first with `amd-ttm --set 100`, since the default of about 62.8 GiB is below the 78 GB it needs\n3. With vLLM use `vllm serve openai/gpt-oss-120b --tool-call-parser openai --enable-auto-tool-choice` per vLLM's recipe, or parse raw output with openai-harmony\n4. Always use the harmony format through the chat template or openai-harmony, the card says it won't work correctly otherwise\n5. Set `Reasoning: low` in the system message for short tasks, medium is the default\n6. Pass the reasoning back after a tool call until the final answer, and keep the analysis channel away from end users\n7. Expect one tool call per turn and loop, don't wait for parallel calls\n\n### Spark-X2.6-4B\n\n1. Quickest start is `llama-server -hf XHToken/Spark-X2.6-4B-GGUF:Q4_K_M --jinja`, which needs llama.cpp b10828 or later, or `ollama run SparkLLM/Spark-X2.6-4B` (8.2 GB, Ollama 0.34.1 or later)\n2. For parsed tool calls serve it with SGLang 0.5.22 or later, `--tool-call-parser spark25 --reasoning-parser qwen3`, since the calls come back as tool_call blocks with arg_key and arg_value pairs\n3. Thinking is on by default. Pass `chat_template_kwargs` with `enable_thinking` false to turn it off, or `reasoning_effort` low for short thinking; the template raises an error on any effort other than high or low\n4. SparkLLM's recommended sampling is temperature 1.0, top_p 0.95 and top_k -1\n5. The template always opens with its own default system line, with the effort instruction, and puts your system message after the tool list\n6. The template raises an error if tool_call arguments are a JSON string, so pass them as an object\n7. Set the context yourself, the card's 1,048,576-token example needs far more memory than the weights\n\n## Questions\n\n### Which is better for AI agents, gpt-oss-120b or Spark-X2.6-4B?\n\ngpt-oss-120b scores 70.2 (BB) on agent readiness against Spark-X2.6-4B's 61 (C), and leads in 3 of 4 scored categories. Spark-X2.6-4B leads on reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gpt-oss-120b-vs-spark-x2-6-4b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gpt-oss-120b-vs-spark-x2-6-4b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gpt-oss-120b\", \"b\": \"spark-x2-6-4b\"}`. 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27B)](https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-thinkingcap-qwen3-8-27b.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-23", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.6", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "gpt-oss-120b vs Spark-X2.6-4B", "url": "" } ], "description": "gpt-oss-120b scores 70.2 (BB) to Spark-X2.6-4B's 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