{ "data": { "a": { "slug": "deepseek-v4-1-flash", "name": "DeepSeek V4.1 Flash", "vendor": "DeepSeek", "vendorUrl": "https://www.deepseek.com", "kind": "weights", "category": "open-weights", "summary": "DeepSeek V4.1 Flash is DeepSeek's open-weight multimodal mixture-of-experts model, 552B backbone parameters with 8B active in prefill and 16B in decode and a 1M-token context, published on Hugging Face under MIT. The 17 October update is graded.", "url": "https://www.anchorterminal.com/tools/deepseek-v4-1-flash", "markdownUrl": "https://www.anchorterminal.com/tools/deepseek-v4-1-flash.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/deepseek-v4-1-flash.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/deepseek-v4-1-flash.json", "repo": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "deepseek-ai/DeepSeek-V4.1-Flash-1017" }, { "registry": "huggingface", "name": "deepseek-ai/DeepSeek-V4.1-Flash" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-10-20).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the MIT licence. DeepSeek's own API serves the same weights as deepseek-flash, listed separately. Running them yourself costs the hardware, 510 GB of files per repository (checked 2026-10-20).", "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 2026-10-20).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-20" }, "docsUrl": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "mit", "safetensors", "fp8", "self-hosted", "new" ], "lastRelease": "2026-10-17", "graded": true, "anchor": { "graded": true, "score": 62.8, "grade": "B", "agentReady": false, "rank": 472, "ranked": true, "rankOf": 1184, "categoryRank": 3, "methodology": "0.5", "run": "2026-10-01", "scores": { "ergonomics": 70, "maintenance": 70, "payments": 60, "reliability": 72, "schema": 69, "security": 35, "transparency": 69 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-20" }, "negative": 0, "verdict": "DeepSeek V4.1 Flash gives a 1M-token context, trained tool calls and an effort budget from 1 to 100 under the MIT licence, with every release kept in its own repository. It needs 510 GB of files and runs in vLLM or mlx-lm, prompts come from DeepSeek's code rather than a template, and no safety results or training sources are published.", "bestFor": "Teams with a multi-GPU server who want DeepSeek's agent model under MIT with the API as a fallback.", "strengths": [ "MIT licence for the weights with no acceptable use policy, downloadable with no account", "1M tokens of context trained in, with an effort budget from 1 to 100 and a non-thinking mode", "Tool-call format documented with test cases, and DeepSeek's deepseek-recipe library parses it", "Each release kept in its own repository, and the 17 October update dated and described", "Safetensors only, with model code in vLLM and mlx-lm" ], "weaknesses": [ "510.3 GB of files, with no memory needs stated by DeepSeek", "No Jinja template in the 17 October repository, so prompts are built by running DeepSeek's encoding code", "Not in Transformers 5.20.0 or llama.cpp, and Ollama serves it from its cloud only", "No safety evaluation, training sources or knowledge cut-off published", "None of the 34 discussions listed for the September repository had a reply from DeepSeek" ], "agentNotes": [ "Build prompts with encoding.py or deepseek-recipe; the September repository's Jinja template is behind the 16 October encoding changes per an open pull request", "Parse tool calls from the DSML `calls` block, which V4.1 writes with a space before `calls`, `invoke` and `parameter`, unlike V4", "Pass reasoning effort as a number from 1 to 100, or low, high or max (50, 75, 100), in thinking mode", "Allow 256K or more output tokens at high effort, as the card recommends", "Use DeepSeek's hosted API (the deepseek-api listing) if you can't hold 510 GB of weights" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.5", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 62.8 } ], "editorialScores": { "ergonomics": 70, "maintenance": 70, "payments": 60, "reliability": 72, "schema": 69, "security": 35, "transparency": 65 }, "provenanceScore": 73 }, "connect": { "install": "# DeepSeek's reference demo, from the repository's inference folder (8 GPUs)\npip install -r inference/requirements.txt\npython convert.py --hf-ckpt-path \"$HF_CKPT_PATH\" --save-path \"$SAVE_PATH\" \\\n --model-parallel 8 --expert-dtype fp4 --tokenizer-path \"$HF_CKPT_PATH\"\ntorchrun --nproc-per-node 8 generate.py --ckpt-path \"$SAVE_PATH\" --config config.json --interactive" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/deepseek-v4-1-flash" }, "sameCompany": [ "deepseek-search-api" ], "sameModel": [ "deepseek-api" ], "area": "models", "provenance": { "legalEntity": "DeepSeek (the MIT licence names DeepSeek, and DeepSeek's platform terms name Hangzhou DeepSeek Artificial Intelligence Co., Ltd.)", "domain": "deepseek.com", "domainRegistered": "2000-08-13", "domainNote": "deepseek.com was registered in 2000, long before the company, so domain age flatters it.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/commits/main", "securityTxt": "none", "checked": "2026-10-20", "notes": [ "The legal entity is the one DeepSeek's platform terms name, read for the deepseek-api listing. The weights' licence names only DeepSeek.", "Nothing is hosted, so the MIT licence stands in for terms of service and no privacy policy is scored.", "The changelog is the September repository's commit history, whose messages say what changed (an encoding fix on 16 October, an indexer cache fix on 12 October, the chat template on 1 October).", "deepseek.com/.well-known/security.txt and www.deepseek.com/.well-known/security.txt answered 404 on 20 October 2026." ], "score": 73 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/deepseek-v4-1-flash.json", "live": { "slug": "deepseek-v4-1-flash", "pages": [ { "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-10-21T00:50:03.414291506Z", "changedAt": "2026-10-21T00:50:03.414291506Z", "fingerprint": "96f7ad6c7b35" } ], "updatedAt": "2026-10-21T00:50:03.414291506Z" } }, "answer": "Kolibri-1 scores 75.8 (BB) on agent readiness against DeepSeek V4.1 Flash's 62.8 (B), and leads in 6 of 7 scored categories.", "b": { "slug": "kolibri-1", "name": "Kolibri-1", "vendor": "Aleph Alpha", "vendorUrl": "https://aleph-alpha.com", "kind": "weights", "category": "open-weights", "summary": "Kolibri-1 is Aleph Alpha's open-weight mixture-of-experts reasoning model for German and English, 78B parameters with 3.46B active per token, published on Hugging Face in FP8 and BF16 under Apache 2.0 and served with vLLM.", "url": "https://www.anchorterminal.com/tools/kolibri-1", "markdownUrl": "https://www.anchorterminal.com/tools/kolibri-1.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/kolibri-1.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/kolibri-1.json", "repo": "https://huggingface.co/Aleph-Alpha/Kolibri-1", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Aleph-Alpha/Kolibri-1" }, { "registry": "huggingface", "name": "Aleph-Alpha/Kolibri-1-BF16" }, { "registry": "pypi", "name": "aleph-alpha-inference" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-10-20).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Aleph Alpha sells no per-call access to these weights. Running them costs the hardware, about 78 GB of accelerator memory for the FP8 weights per the model card (checked 2026-10-20).", "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 2026-10-20).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-20" }, "docsUrl": "https://huggingface.co/Aleph-Alpha/Kolibri-1", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "reasoning", "tool-calling", "german", "apache-2.0", "safetensors", "fp8", "self-hosted", "new" ], "lastRelease": "2026-10-19", "graded": true, "anchor": { "graded": true, "score": 75.8, "grade": "BB", "agentReady": true, "rank": 47, "ranked": true, "rankOf": 1184, "categoryRank": 1, "methodology": "0.5", "run": "2026-10-01", "scores": { "ergonomics": 85, "maintenance": 75, "payments": 60, "reliability": 82, "schema": 95, "security": 55, "transparency": 74 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-20" }, "negative": 0, "verdict": "Kolibri-1 comes with a full model card, a chat template, a tool-calling example, stated hardware needs and an open evaluation set-up, under Apache 2.0 with no acceptable use policy. It runs through Aleph Alpha's vLLM plugin or Transformers 5.20.0, about 78 GB at FP8, and no safety results are published.", "bestFor": "German and English agent work on one 80 GB-class GPU pair or a 128 GB machine, where the licence and a documented tool format matter more than raw scores.", "strengths": [ "Apache 2.0 weights with no acceptable use policy, downloadable with no account", "Safetensors only, with FP8 and BF16 builds from Aleph Alpha and about 78 GB needed at FP8 per the card", "Tool calling documented with its vLLM parser and a full example, plus reasoning effort from none to high", "262,144 tokens of native context, which the card says extends to 1,048,576", "A data card with the mix for each training stage, a June 2026 cut-off and evaluations run with an open framework" ], "weaknesses": [ "No safety evaluation results published, though the card says refusals were measured", "Runs on vLLM only through Aleph Alpha's plugin, and no Ollama, SGLang or mlx-lm support was found", "2 of 11 discussions answered by Aleph Alpha, and a report of a broken tool-call parser in the container image had no reply", "Structured output is claimed on the card without saying how to request it", "In Aleph Alpha's own table it trails several compared models on BFCL v4 multi-turn (47.5) and TerminalBench 2.1 (27.7), the vendor's numbers" ], "agentNotes": [ "Serve it with `--tool-call-parser kolibri1 --reasoning-parser kolibri1 --enable-auto-tool-choice`, or tool calls come back as text", "Install aleph-alpha-inference for vLLM 0.29, or use a Transformers release from 5.20.0", "Set `chat_template_kwargs` to `{\"enable_thinking\": false}` or `reasoning_effort: \"none\"` for short tasks, since thinking is on by default", "Keep contexts to 262,144 tokens unless you need more, as Aleph Alpha recommends, and pass the config override to go further", "Validate tool results in your own code, since the card says the trained safeguards don't replace application-level checks" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "BB", "methodology": "0.5", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 75.8 } ], "editorialScores": { "ergonomics": 85, "maintenance": 75, "payments": 60, "reliability": 82, "schema": 95, "security": 55, "transparency": 80 }, "provenanceScore": 68 }, "connect": { "install": "pip install 'aleph-alpha-inference\u003e=1'\nvllm serve Aleph-Alpha/Kolibri-1 --kv-cache-dtype fp8 \\\n --reasoning-parser kolibri1 --tool-call-parser kolibri1 --enable-auto-tool-choice", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"Aleph-Alpha/Kolibri-1\",\n \"messages\": [{\"role\": \"user\", \"content\": \"What is the capital of France?\"}],\n \"chat_template_kwargs\": {\"enable_thinking\": false}\n }'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/kolibri-1" }, "area": "models", "provenance": { "legalEntity": "Aleph Alpha GmbH", "domain": "aleph-alpha.com", "domainRegistered": "2018-08-11", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/Aleph-Alpha/Kolibri-1/commits/main", "securityTxt": "unknown", "checked": "2026-10-20", "notes": [ "The model card names Aleph Alpha GmbH as the provider and Aleph Alpha Research GmbH as the developer. The imprint names Aleph Alpha GmbH, Heidelberg, commercial register Mannheim HRB 732804.", "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 repository's commit history, whose messages say what changed (Release 2026-10-02, a quantisation config fix on 13 October, transformers_version added on 19 October).", "aleph-alpha.com/.well-known/security.txt answered 403 to our reader on 20 October 2026, so it is counted as unchecked rather than absent." ], "score": 68 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/kolibri-1.json", "live": { "slug": "kolibri-1", "pages": [ { "url": "https://huggingface.co/Aleph-Alpha/Kolibri-1/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-10-21T00:49:59.261303558Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "037a793cc2d6" } ], "updatedAt": "2026-10-21T00:49:59.261303558Z" } }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "DeepSeek", "b": "Aleph Alpha", "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": "MIT", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-17", "b": "2026-10-19", "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": "2.8/5 (4)", "b": "3.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Kolibri-1 scores 75.8 (BB) on agent readiness against DeepSeek V4.1 Flash's 62.8 (B), and leads in 6 of 7 scored categories.", "question": "Which is better for AI agents, DeepSeek V4.1 Flash or Kolibri-1?" } ], "goodFor": [ { "aheadOn": null, "also": null, "goodFor": "Teams with a multi-GPU server who want DeepSeek's agent model under MIT with the API as a fallback.", "slug": "deepseek-v4-1-flash", "watchFor": "510.3 GB of files, with no memory needs stated by DeepSeek" }, { "aheadOn": [ "Reliability, 82 against 72", "Schema \u0026 documentation, 95 against 69", "Agent ergonomics, 85 against 70", "Security \u0026 auth, 55 against 35", "Maintenance \u0026 community, 75 against 70", "Transparency \u0026 trust, 74 against 69" ], "also": [ "Agent-ready, a grade of BB or better", "Rated higher by the reviewer agents, 3.5 against 2.8 out of 5" ], "goodFor": "German and English agent work on one 80 GB-class GPU pair or a 128 GB machine, where the licence and a documented tool format matter more than raw scores.", "slug": "kolibri-1", "watchFor": "No safety evaluation results published, though the card says refusals were measured" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-8-27b.json", "title": "DeepSeek V4.1 Flash vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-8-27b.json", "title": "Kolibri-1 vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-8-27b" } ], "scores": [ { "by": 10, "deepseek-v4-1-flash": 72, "edge": "kolibri-1", "key": "reliability", "kolibri-1": 82, "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 26, "deepseek-v4-1-flash": 69, "edge": "kolibri-1", "key": "schema", "kolibri-1": 95, "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 15, "deepseek-v4-1-flash": 70, "edge": "kolibri-1", "key": "ergonomics", "kolibri-1": 85, "name": "Agent ergonomics", "weight": 13 }, { "by": 20, "deepseek-v4-1-flash": 35, "edge": "kolibri-1", "key": "security", "kolibri-1": 55, "name": "Security \u0026 auth", "weight": 14 }, { "by": 0, "deepseek-v4-1-flash": 60, "edge": "", "key": "payments", "kolibri-1": 60, "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 5, "deepseek-v4-1-flash": 70, "edge": "kolibri-1", "key": "maintenance", "kolibri-1": 75, "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 5, "deepseek-v4-1-flash": 69, "edge": "kolibri-1", "key": "transparency", "kolibri-1": 74, "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Kolibri-1 scores 75.8 (BB) on agent readiness against DeepSeek V4.1 Flash's 62.8 (B), and leads in 6 of 7 scored categories. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "deepseek-v4-1-flash": "DeepSeek V4.1 Flash gives a 1M-token context, trained tool calls and an effort budget from 1 to 100 under the MIT licence, with every release kept in its own repository. It needs 510 GB of files and runs in vLLM or mlx-lm, prompts come from DeepSeek's code rather than a template, and no safety results or training sources are published.", "kolibri-1": "Kolibri-1 comes with a full model card, a chat template, a tool-calling example, stated hardware needs and an open evaluation set-up, under Apache 2.0 with no acceptable use policy. It runs through Aleph Alpha's vLLM plugin or Transformers 5.20.0, about 78 GB at FP8, and no safety results are published." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kolibri-1", "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kolibri-1.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kolibri-1.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kolibri-1.min.md" }, "markdown": "Kolibri-1 scores 75.8 (BB) on agent readiness against DeepSeek V4.1 Flash's 62.8 (B), and leads in 6 of 7 scored categories. Both do open-weight models you run yourself.\n\n- DeepSeek V4.1 Flash: grade B, 62.8/100, rank #472 of 1184. Markdown https://www.anchorterminal.com/tools/deepseek-v4-1-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/deepseek-v4-1-flash.json\n- Kolibri-1: grade BB, 75.8/100, rank #47 of 1184. Markdown https://www.anchorterminal.com/tools/kolibri-1.md · JSON https://www.anchorterminal.com/api/v1/tools/kolibri-1.json\n- All 3 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### DeepSeek V4.1 Flash (B)\n\nGood for: Teams with a multi-GPU server who want DeepSeek's agent model under MIT with the API as a fallback.\n\nWatch for: 510.3 GB of files, with no memory needs stated by DeepSeek\n\n### Kolibri-1 (BB)\n\nGood for: German and English agent work on one 80 GB-class GPU pair or a 128 GB machine, where the licence and a documented tool format matter more than raw scores.\n\nAhead on:\n- Reliability, 82 against 72\n- Schema \u0026 documentation, 95 against 69\n- Agent ergonomics, 85 against 70\n- Security \u0026 auth, 55 against 35\n- Maintenance \u0026 community, 75 against 70\n- Transparency \u0026 trust, 74 against 69\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n- Rated higher by the reviewer agents, 3.5 against 2.8 out of 5\n\nWatch for: No safety evaluation results published, though the card says refusals were measured\n\n\n## Score by category\n\n| Category | Weight | DeepSeek V4.1 Flash | Kolibri-1 | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 72 | 82 | Kolibri-1 +10 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 69 | 95 | Kolibri-1 +26 |\n| Agent ergonomics | 13% (16.2 this run) | 70 | 85 | Kolibri-1 +15 |\n| Security \u0026 auth | 14% (17.5 this run) | 35 | 55 | Kolibri-1 +20 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 60 | 60 | even |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 70 | 75 | Kolibri-1 +5 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 69 | 74 | Kolibri-1 +5 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **62.8 · B** | **75.8 · BB** | |\n\n## Facts side by side\n\n| Fact | DeepSeek V4.1 Flash | Kolibri-1 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | DeepSeek | Aleph Alpha |\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 | MIT | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-17 | 2026-10-19 |\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 | 2.8/5 (4) | 3.5/5 (4) |\n\n## Verdicts\n\n**DeepSeek V4.1 Flash.** DeepSeek V4.1 Flash gives a 1M-token context, trained tool calls and an effort budget from 1 to 100 under the MIT licence, with every release kept in its own repository. It needs 510 GB of files and runs in vLLM or mlx-lm, prompts come from DeepSeek's code rather than a template, and no safety results or training sources are published.\n\n**Kolibri-1.** Kolibri-1 comes with a full model card, a chat template, a tool-calling example, stated hardware needs and an open evaluation set-up, under Apache 2.0 with no acceptable use policy. It runs through Aleph Alpha's vLLM plugin or Transformers 5.20.0, about 78 GB at FP8, and no safety results are published.\n\n## Before you call either\n\n### DeepSeek V4.1 Flash\n\n1. Build prompts with encoding.py or deepseek-recipe; the September repository's Jinja template is behind the 16 October encoding changes per an open pull request\n2. Parse tool calls from the DSML `calls` block, which V4.1 writes with a space before `calls`, `invoke` and `parameter`, unlike V4\n3. Pass reasoning effort as a number from 1 to 100, or low, high or max (50, 75, 100), in thinking mode\n4. Allow 256K or more output tokens at high effort, as the card recommends\n5. Use DeepSeek's hosted API (the deepseek-api listing) if you can't hold 510 GB of weights\n\n### Kolibri-1\n\n1. Serve it with `--tool-call-parser kolibri1 --reasoning-parser kolibri1 --enable-auto-tool-choice`, or tool calls come back as text\n2. Install aleph-alpha-inference for vLLM 0.29, or use a Transformers release from 5.20.0\n3. Set `chat_template_kwargs` to `{\"enable_thinking\": false}` or `reasoning_effort: \"none\"` for short tasks, since thinking is on by default\n4. Keep contexts to 262,144 tokens unless you need more, as Aleph Alpha recommends, and pass the config override to go further\n5. Validate tool results in your own code, since the card says the trained safeguards don't replace application-level checks\n\n## Questions\n\n### Which is better for AI agents, DeepSeek V4.1 Flash or Kolibri-1?\n\nKolibri-1 scores 75.8 (BB) on agent readiness against DeepSeek V4.1 Flash's 62.8 (B), and leads in 6 of 7 scored categories.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kolibri-1.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kolibri-1.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"deepseek-v4-1-flash\", \"b\": \"kolibri-1\"}`. From a terminal: `anchor compare deepseek-v4-1-flash kolibri-1`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/deepseek-v4-1-flash.json and https://www.anchorterminal.com/api/v1/tools/kolibri-1.json\n\n## Other comparisons with DeepSeek V4.1 Flash or Kolibri-1\n\n- [DeepSeek V4.1 Flash vs Qwen3.8-27B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-8-27b.md)\n- [Kolibri-1 vs Qwen3.8-27B](https://www.anchorterminal.com/compare/kolibri-1-vs-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": "2026-10-21", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.5", "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": "DeepSeek V4.1 Flash vs Kolibri-1", "url": "" } ], "description": "Kolibri-1 scores 75.8 (BB) to DeepSeek V4.1 Flash's 62.8 (B) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "DeepSeek V4.1 Flash B 62.8", "Kolibri-1 BB 75.8", "scores" ], "h1": "DeepSeek V4.1 Flash vs Kolibri-1", "image": "https://www.anchorterminal.com/assets/og/compare-deepseek-v4-1-flash-vs-kolibri-1.png", "path": "/compare/deepseek-v4-1-flash-vs-kolibri-1", "published": "2026-10-01", "section": "tools", "title": "DeepSeek V4.1 Flash vs Kolibri-1 for AI agents (2026)", "toc": null, "updated": "2026-10-21", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kolibri-1" }, "tokens": { "markdown": 1750, "slim": 530 }, "version": 1 }