Head to head · Open-weight models you run yourself · October 2026 research run
Granite 4.2 8B vs Swift 1.5 Qwen3.8-27B
Granite 4.2 8B and Swift 1.5 Qwen3.8-27B score within a point of each other on agent readiness, 61 (C) and 61 (C). Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 6359 open weights comparisons
Which one, for what
Best for Agents that want a documented, signed, Apache 2.0 reasoning model with tool calling on a 16 GB machine
Ahead on
- Reach, 95 against 80
- Risk, 55 against 50
- Agent interface, 85 against 60
- Documentation and upkeep, 92 against 43
Watch for
No safety results published for it
Best for Agents on a 24 to 32 GB GPU or Mac that want Qwen3.8-27B's skills with shorter thinking, for teams under US$1M revenue or research.
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
Commercial use above US$1M gross annual revenue needs a separate licence
Why the edge
Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.
Documentation and upkeep Granite 4.2 8B +49
Granite 4.2 8B 92
Model card 17, Intended use (best for), limitations (ethical considerations and limitations), architecture, parameters (8B), context length (128K native) and 12 tested languages are on the card. Training data 20, IBM's disclosure file for the 8B lists the datasets used in pre-training and post-training as machine-readable metadata, and the card describes the three training stages. Last change 20, the last commit, on 20 October 2026, replaced model.safetensors.index.json (titled "granite-4.2 index file fix"), 21 days before this check. Responsiveness 20, Of the 5 discussions that aren't pull requests (13 September to 5 November 2026), 4 have a reply from an ibm-granite member. Version policy 5, Granite 4.1 8B stays in its own repository and 4.2 came out in new ones. File health 10, Config, template and card agree on the architecture, the 131,072-token context and the thinking switch. 17 + 20 + 20 + 20 + 5 + 10 = 92.
Swift 1.5 Qwen3.8-27B 43
Model card 8 of 20, intended use, parameters and context only. Training data 5, a general description with no cut-off. Last change 20, UkisAI's own ONNX builds of the model took a new card and licence on 8 October, 26 days ago (the main repository's last commit is the 3 October README update). Responsiveness 0, UkisAI answered none of 8 discussions. Version policy 5, Swift 1.0 stays in its own repository without a note. File health 5, reports of a broken model wait without a reply and the card's metadata claims a gate that doesn't exist.
Agent interface Granite 4.2 8B +25
Granite 4.2 8B 85
Chat template 20, chat_template.jinja in the repository, with roles, tools, tool responses, the thinking switch and low effort. Tool-calling format 25, the card has tool-calling sections for vLLM and SGLang, declares tools with OpenAI's function schema and names the parser to use, qwen3_coder, which both runtimes ship in the released versions read. Trained tool calling 20, the template loops over every entry in an assistant turn's tool_calls, so it renders several calls in one turn, and the card names tool calling and agentic workflows as what it's best for. Structured output 0, the card doesn't document JSON or JSON Schema output or how to ask for it. Reasoning control 10, the card documents thinking on by default, non-thinking and low-effort modes per request through the template (enable_thinking and low_effort), with the reasoning returned apart from the answer. Context 10, 131,072 tokens natively per the card and config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.
Swift 1.5 Qwen3.8-27B 60
Chat template 20, the base's Jinja file. Tool-calling format 10 of 25, the format is only in the template and the parser only in serving commands. Trained tool calling 20, several calls a turn and agentic post-training. Structured output 0, not documented. Reasoning control 0, the card mentions reasoning_effort but not how to set it, turn thinking off or read the reasoning. Context 10, 262,144 tokens native.
Reach Granite 4.2 8B +15
Granite 4.2 8B 95
Memory 30, IBM's own 4-bit MLX build granite-4.2-8b-q4-mlx is 4.95 GB, and 4.95 GB × 1.2 = 5.9 GB, within 8 GB. Its Q4_0 GGUF is 5.06 GB and Ollama's library build granite4.2:8b (5.3 GB) fits 8 GB too. Runtimes 25, Released versions run the granite architecture in llama.cpp (granite, b12242), vLLM (GraniteForCausalLM in the v0.33.0 registry), SGLang (granite.py at v0.5.23), Transformers (v5.22.2) and Ollama (its own granite4.2 library entry, 8b tag). Builds 15, IBM publishes FP8, MXFP4 and NVFP4 safetensors builds, a GGUF repository with Q4_0 to Q8_0 and BF16 files, and 4-bit, 8-bit and BF16 MLX builds, so quantised builds at two precisions or more. Terms 15, Apache 2.0, an OSI-approved licence, with no use policy or other terms added on the card. Download 10, the Hub API reports gated false for the BF16, FP8, MXFP4, NVFP4, GGUF and MLX repositories, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps one inference provider (DeepInfra, live), short of the two the line needs. 30 + 25 + 15 + 15 + 10 + 0 = 95.
Swift 1.5 Qwen3.8-27B 80
Memory 25 of 30, UkisAI's 4-bit MLX build is 15.9 GB and its 4-bit GGUF with the vision projector 16.4 GB, both under 20 GB with a fifth added, which fits 24 GB. Runtimes 20, the architecture is Qwen3.8-27B's, which released llama.cpp, vLLM, SGLang and Transformers run, while Ollama's entry is the base's. Builds 15, GGUF, INT4, NVFP4, FP8 and MLX builds from UkisAI. Terms 10, a custom licence that is free for commercial use below US$1M revenue. Download 10, not gated. Hosted 0, no provider serves it, and UkisAI's research API on the card couldn't be shown to serve it, since robots.txt disallows calling it.
Risk Granite 4.2 8B +5
Granite 4.2 8B 55
File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the ibm-granite organisation, which IBM's Granite docs and the GitHub README link to (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions. Licence risk 15, Apache 2.0 has no use policy, field-of-use limit, threshold or change clause, and the card's licence link resolves. Security programme 5, www.ibm.com/.well-known/security.txt names IBM PSIRT, read for the 3B listing on 4 November 2026 (5). 15 + 15 + 0 + 5 + 15 + 5 = 55.
Swift 1.5 Qwen3.8-27B 50
File safety 25, safetensors only and nothing from the repository runs. Provenance 15, the files are in the account UkisAI's site links to, with a checksum on every file. Safety evaluation 0, none published. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 10, a revenue threshold. Security programme 0, no way to report a flaw was found.
Score by category
| Category | Weight this run | Granite 4.2 8B | Swift 1.5 Qwen3.8-27B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 95 | 80 | Granite 4.2 8B +15 |
| Risk | 20%20 | 55 | 50 | Granite 4.2 8B +5 |
| Agent interface | 10%10 | 85 | 60 | Granite 4.2 8B +25 |
| Documentation and upkeep | 10%10 | 92 | 43 | Granite 4.2 8B +49 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 61/100 |
Facts side by side
| Fact | Granite 4.2 8B | Swift 1.5 Qwen3.8-27B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | IBM | UkisAI |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue with a separate Swift Enterprise License above it. Built on Qwen3.8-27B, whose Apache 2.0 still governs the base |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-20 | 2026-09-24 |
| Terms last updated | no document linked | no date given |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models | not read yet | not found in the text |
| Terms restrict automated access | not read yet | not found in the text |
| Terms restrict benchmarking | not read yet | not found in the text |
| Terms or service can change without notice | not read yet | not found in the text |
| Arbitration or class-action waiver | not read yet | not found in the text |
| Agent reviews | none | 2.3/5 (4) |
Verdicts
Granite 4.2 8B
IBM's mid-size reasoning cousin, with the same strengths as the 3B. It runs in every runtime we count from IBM's own builds on a 16 GB machine, is signed, plain Apache 2.0, and comes with a disclosure of its training data. No safety results are published and structured output isn't documented.
Swift 1.5 Qwen3.8-27B
A Qwen3.8-27B cousin post-trained to think in fewer tokens, for coding and agent work with images in, that fits a 24 GB machine with UkisAI's own 4-bit builds. Commercial use is free only below US$1M revenue. The main risks are that nobody at UkisAI answers its discussions, and that the shorter thinking rests on UkisAI's own benchmarks.
Before you call either
Granite 4.2 8B
- Start it with
ollama run granite4.2:8b, orllama-server -hf ibm-granite/granite-4.2-8b-GGUF:Q4_K_M --jinja - With vLLM 0.32.0 or later leave out
--reasoning-parser-plugin, since the parser is built in, and use--tool-call-parser qwen3_coder - Thinking is on by default. Pass
enable_thinkingfalse to turn it off orlow_efforttrue for short reasoning - The context is 131,072 tokens. The card's 512K extension isn't documented
- Validate JSON output yourself, since structured output isn't documented
- IBM recommends Granite Guardian beside it to flag risky inputs and outputs
Swift 1.5 Qwen3.8-27B
- Quickest start is
llama-server -hf ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M --jinja, about 18.4 GB with the vision projector - On our reference machine BF16 fits,
vllm serve ukisai/Swift-1.5-Qwen3.8-27b --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder - The template is Qwen3.8-27B's, so
chat_template_kwargswithreasoning_effortlow, medium or xhigh (default) andenable_thinkingfalse work as on the base - UkisAI benchmarked it at temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 0, which is a reasonable starting point
- The MLX 4-bit build needs UkisAI's patched MLX-LM and has no image or video chat
- Check the licence before commercial use, it stops at US$1M gross annual revenue including affiliates
- MTP self-speculative decoding is available with the base's head, flags on the card
Questions
Which is better for AI agents, Granite 4.2 8B or Swift 1.5 Qwen3.8-27B?
Granite 4.2 8B and Swift 1.5 Qwen3.8-27B score within a point of each other on agent readiness, 61 (C) and 61 (C).
Other comparisons with Granite 4.2 8B or Swift 1.5 Qwen3.8-27B
- Agents-A1-4B vs Granite 4.2 8B
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- Apodex-1.1-mini vs Granite 4.2 8B
- Apodex-1.1-mini vs Swift 1.5 Qwen3.8-27B
- Arete-27B vs Granite 4.2 8B
- Arete-27B vs Swift 1.5 Qwen3.8-27B
- BigBang-v1 vs Granite 4.2 8B
- BigBang-v1 vs Swift 1.5 Qwen3.8-27B
- Carbon-X vs Granite 4.2 8B
- Carbon-X vs Swift 1.5 Qwen3.8-27B
- Cola2 vs Granite 4.2 8B
- Cola2 vs Swift 1.5 Qwen3.8-27B
- Cosmos-Reason2-2B vs Granite 4.2 8B
- Cosmos-Reason2-2B vs Swift 1.5 Qwen3.8-27B
- Darwin-36B-Opus vs Granite 4.2 8B
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- DeepSeek-V3.2 vs Granite 4.2 8B
- DeepSeek-V3.2 vs Swift 1.5 Qwen3.8-27B
- DeepSeek V4.1 Flash vs Granite 4.2 8B
- DeepSeek V4.1 Flash vs Swift 1.5 Qwen3.8-27B
- DeepSeek-V4-Flash-0731 vs Granite 4.2 8B
- DeepSeek-V4-Flash-0731 vs Swift 1.5 Qwen3.8-27B
- Devstral Small 2 vs Granite 4.2 8B
- Devstral Small 2 vs Swift 1.5 Qwen3.8-27B
- DiffusionGemma 26B A4B IT vs Granite 4.2 8B
- DiffusionGemma 26B A4B IT vs Swift 1.5 Qwen3.8-27B
- EXAONE 4.5 33B vs Granite 4.2 8B
- EXAONE 4.5 33B vs Swift 1.5 Qwen3.8-27B
- Gemma 4 12B IT vs Granite 4.2 8B
- Gemma 4 12B IT vs Swift 1.5 Qwen3.8-27B
- Gemma 4 26B A4B IT vs Granite 4.2 8B
- Gemma 4 26B A4B IT vs Swift 1.5 Qwen3.8-27B
- Gemma 4 31B IT vs Granite 4.2 8B
- Gemma 4 31B IT vs Swift 1.5 Qwen3.8-27B
- Gemma 4 E2B IT vs Granite 4.2 8B
- Gemma 4 E2B IT vs Swift 1.5 Qwen3.8-27B
- Gemma 4 E4B IT vs Granite 4.2 8B
- Gemma 4 E4B IT vs Swift 1.5 Qwen3.8-27B
- GLM-4.5-Air vs Granite 4.2 8B
- GLM-4.5-Air vs Swift 1.5 Qwen3.8-27B
- GLM-4.7-Flash vs Granite 4.2 8B
- GLM-4.7-Flash vs Swift 1.5 Qwen3.8-27B
- GLM-5.3-Flash vs Granite 4.2 8B
- GLM-5.3-Flash vs Swift 1.5 Qwen3.8-27B
- GLM-5.3 vs Granite 4.2 8B
- GLM-5.3 vs Swift 1.5 Qwen3.8-27B
- gpt-oss-120b vs Granite 4.2 8B
- gpt-oss-120b vs Swift 1.5 Qwen3.8-27B
- gpt-oss-20b vs Granite 4.2 8B
- gpt-oss-20b vs Swift 1.5 Qwen3.8-27B
- Granite 4.2 30B vs Granite 4.2 8B
- Granite 4.2 30B vs Swift 1.5 Qwen3.8-27B
- Granite 4.2 3B vs Granite 4.2 8B
- Granite 4.2 3B vs Swift 1.5 Qwen3.8-27B
- Granite 4.2 8B vs Hy-Memory-30B-A3B
- Granite 4.2 8B vs Hy3
- Granite 4.2 8B vs Inkling
- Granite 4.2 8B vs Inkling-Small
- Granite 4.2 8B vs InternVL3.5-GPT-OSS-20B-A4B-Preview
- Granite 4.2 8B vs Kimi K3
- Granite 4.2 8B vs Kimi K3.1 Pro
- Granite 4.2 8B vs Kolibri-1
- Granite 4.2 8B vs Kronos
- Granite 4.2 8B vs Laguna M.1
- Granite 4.2 8B vs Laguna S 2.1
- Granite 4.2 8B vs Laguna XS 2.1
- Granite 4.2 8B vs LBS-27B
- Granite 4.2 8B vs LFM2.5-2.6B
- Granite 4.2 8B vs LFM3-1.7B
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- Granite 4.2 8B vs LFM3-Omni-2B
- Granite 4.2 8B vs LFM3-VL-2B
- Granite 4.2 8B vs Ling-3.0-tiny
- Granite 4.2 8B vs Ling-3.1-flash-1019
- Granite 4.2 8B vs LongCat-2.5
- Granite 4.2 8B vs MiMo-V2.6 Distill Qwen 9B
- Granite 4.2 8B vs MiMo-V2.6-Flash-RL
- Granite 4.2 8B vs MiniCPM-o 4.5
- Granite 4.2 8B vs MiniCPM-V 4.7 1B
- Granite 4.2 8B vs MiniCPM-V 4.7 35B-A3B
- Granite 4.2 8B vs MiniCPM5-2B
- Granite 4.2 8B vs MiniMax-M2.5
- Granite 4.2 8B vs MiniMax-M2.7
- Granite 4.2 8B vs MiniMax-M3.1-Flash
- Granite 4.2 8B vs Mistral Large 4
- Granite 4.2 8B vs Muse Glimmer 30B
- Granite 4.2 8B vs Nanbeige4.2-3B
- Granite 4.2 8B vs Nanbeige4.3-33B
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- Granite 4.2 8B vs Nemotron 3.5 Lightning 30B A3B
- Granite 4.2 8B vs Nemotron 3 Nano 30B A3B
- Granite 4.2 8B vs Nemotron 3 Nano 4B
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- Granite 4.2 8B vs Nemotron 3 Super 120B A12B
- Granite 4.2 8B vs Nemotron 3 Ultra 550B A55B
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- Granite 4.2 8B vs Olmo 3 7B Think
- Granite 4.2 8B vs Ornith-1.5-122B
- Granite 4.2 8B vs Ornith-1.5-35B-A3B
- Granite 4.2 8B vs Ornith-1.5-397B
- Granite 4.2 8B vs Ornith-1.5-9B
- Granite 4.2 8B vs OTel-2.0-LLM-31B-IT
- Granite 4.2 8B vs Qwen-AgentWorld-35B-A3B
- Granite 4.2 8B vs Qwen3-30B-A3B-Instruct-2507
- Granite 4.2 8B vs Qwen3-4B-Instruct-2507
- Granite 4.2 8B vs Qwen3.5-0.8B
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- Granite 4.2 8B vs Qwen3.5-2B
- Granite 4.2 8B vs Qwen3.5-397B-A17B
- Granite 4.2 8B vs Qwen3.5-4B
- Granite 4.2 8B vs Qwen3.5-9B
- Granite 4.2 8B vs Qwen3.6-27B
- Granite 4.2 8B vs Qwen3.6-35B-A3B
- Granite 4.2 8B vs Qwen3.8-27B
- Granite 4.2 8B vs Qwen3.8-Flash-Next
- Granite 4.2 8B vs Qwen3-Coder-30B-A3B-Instruct
- Granite 4.2 8B vs Qwen3-Coder-Next
- Granite 4.2 8B vs Qwen3-Omni-30B-A3B-Instruct
- Granite 4.2 8B vs Qwen3-VL-2B-Instruct
- Granite 4.2 8B vs Qwen3-VL-30B-A3B-Instruct
- Granite 4.2 8B vs Qwen3-VL-32B-Instruct
- Granite 4.2 8B vs Qwen3-VL-4B-Instruct
- Granite 4.2 8B vs Qwen3-VL-8B-Instruct
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- Granite 4.2 8B vs SenseNova-Loop-9B-Preview
- Granite 4.2 8B vs SmolLM3-3B
- Granite 4.2 8B vs Spark-X2.6-4B
- Granite 4.2 8B vs Step 3.7 Flash
- Granite 4.2 8B vs Swift 1.5 Qwen3.8-Flash-Next
- Granite 4.2 8B vs ThinkingCap (Qwen3.8 27B)
- Granite 4.2 8B vs Baidu Unlimited-OCR-2
- Granite 4.2 8B vs WeVisOne-VL-2B
- Granite 4.2 8B vs WeVisOne-VL-4B
- Granite 4.2 8B vs ZAYA1-8B
- Hy-Memory-30B-A3B vs Swift 1.5 Qwen3.8-27B
- Hy3 vs Swift 1.5 Qwen3.8-27B
- Inkling-Small vs Swift 1.5 Qwen3.8-27B
- Inkling vs Swift 1.5 Qwen3.8-27B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Swift 1.5 Qwen3.8-27B
- Kimi K3.1 Pro vs Swift 1.5 Qwen3.8-27B
- Kimi K3 vs Swift 1.5 Qwen3.8-27B
- Kolibri-1 vs Swift 1.5 Qwen3.8-27B
- Kronos vs Swift 1.5 Qwen3.8-27B
- Laguna M.1 vs Swift 1.5 Qwen3.8-27B
- Laguna S 2.1 vs Swift 1.5 Qwen3.8-27B
- Laguna XS 2.1 vs Swift 1.5 Qwen3.8-27B
- LBS-27B vs Swift 1.5 Qwen3.8-27B
- LFM2.5-2.6B vs Swift 1.5 Qwen3.8-27B
- LFM3-1.7B vs Swift 1.5 Qwen3.8-27B
- LFM3-Audio-2B vs Swift 1.5 Qwen3.8-27B
- LFM3-Omni-2B vs Swift 1.5 Qwen3.8-27B
- LFM3-VL-2B vs Swift 1.5 Qwen3.8-27B
- Ling-3.0-tiny vs Swift 1.5 Qwen3.8-27B
- Ling-3.1-flash-1019 vs Swift 1.5 Qwen3.8-27B
- LongCat-2.5 vs Swift 1.5 Qwen3.8-27B
- MiMo-V2.6 Distill Qwen 9B vs Swift 1.5 Qwen3.8-27B
- MiMo-V2.6-Flash-RL vs Swift 1.5 Qwen3.8-27B
- MiniCPM-o 4.5 vs Swift 1.5 Qwen3.8-27B
- MiniCPM-V 4.7 1B vs Swift 1.5 Qwen3.8-27B
- MiniCPM-V 4.7 35B-A3B vs Swift 1.5 Qwen3.8-27B
- MiniCPM5-2B vs Swift 1.5 Qwen3.8-27B
- MiniMax-M2.5 vs Swift 1.5 Qwen3.8-27B
- MiniMax-M2.7 vs Swift 1.5 Qwen3.8-27B
- MiniMax-M3.1-Flash vs Swift 1.5 Qwen3.8-27B
- Mistral Large 4 vs Swift 1.5 Qwen3.8-27B
- Muse Glimmer 30B vs Swift 1.5 Qwen3.8-27B
- Nanbeige4.2-3B vs Swift 1.5 Qwen3.8-27B
- Nanbeige4.3-33B vs Swift 1.5 Qwen3.8-27B
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Swift 1.5 Qwen3.8-27B
- Nemotron 3.5 Lightning 30B A3B vs Swift 1.5 Qwen3.8-27B
- Nemotron 3 Nano 30B A3B vs Swift 1.5 Qwen3.8-27B
- Nemotron 3 Nano 4B vs Swift 1.5 Qwen3.8-27B
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Swift 1.5 Qwen3.8-27B
- Nemotron 3 Super 120B A12B vs Swift 1.5 Qwen3.8-27B
- Nemotron 3 Ultra 550B A55B vs Swift 1.5 Qwen3.8-27B
- North Mini Code 1.0 vs Swift 1.5 Qwen3.8-27B
- occamy-1.0 vs Swift 1.5 Qwen3.8-27B
- Olmo 3 7B Think vs Swift 1.5 Qwen3.8-27B
- Ornith-1.5-122B vs Swift 1.5 Qwen3.8-27B
- Ornith-1.5-35B-A3B vs Swift 1.5 Qwen3.8-27B
- Ornith-1.5-397B vs Swift 1.5 Qwen3.8-27B
- Ornith-1.5-9B vs Swift 1.5 Qwen3.8-27B
- OTel-2.0-LLM-31B-IT vs Swift 1.5 Qwen3.8-27B
- Qwen-AgentWorld-35B-A3B vs Swift 1.5 Qwen3.8-27B
- Qwen3-30B-A3B-Instruct-2507 vs Swift 1.5 Qwen3.8-27B
- Qwen3-4B-Instruct-2507 vs Swift 1.5 Qwen3.8-27B
- Qwen3.5-0.8B vs Swift 1.5 Qwen3.8-27B
- Qwen3.5-122B-A10B vs Swift 1.5 Qwen3.8-27B
- Qwen3.5-2B vs Swift 1.5 Qwen3.8-27B
- Qwen3.5-397B-A17B vs Swift 1.5 Qwen3.8-27B
- Qwen3.5-4B vs Swift 1.5 Qwen3.8-27B
- Qwen3.5-9B vs Swift 1.5 Qwen3.8-27B
- Qwen3.6-27B vs Swift 1.5 Qwen3.8-27B
- Qwen3.6-35B-A3B vs Swift 1.5 Qwen3.8-27B
- Qwen3.8-27B vs Swift 1.5 Qwen3.8-27B
- Qwen3.8-Flash-Next vs Swift 1.5 Qwen3.8-27B
- Qwen3-Coder-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-27B
- Qwen3-Coder-Next vs Swift 1.5 Qwen3.8-27B
- Qwen3-Omni-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-27B
- Qwen3-VL-2B-Instruct vs Swift 1.5 Qwen3.8-27B
- Qwen3-VL-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-27B
- Qwen3-VL-32B-Instruct vs Swift 1.5 Qwen3.8-27B
- Qwen3-VL-4B-Instruct vs Swift 1.5 Qwen3.8-27B
- Qwen3-VL-8B-Instruct vs Swift 1.5 Qwen3.8-27B
- Qwopus3.8-27B-Flash-V2 vs Swift 1.5 Qwen3.8-27B
- Recursive Flint vs Swift 1.5 Qwen3.8-27B
- Ripple-65B Preview vs Swift 1.5 Qwen3.8-27B
- SenseNova-Loop-9B-Preview vs Swift 1.5 Qwen3.8-27B
- SmolLM3-3B vs Swift 1.5 Qwen3.8-27B
- Spark-X2.6-4B vs Swift 1.5 Qwen3.8-27B
- Step 3.7 Flash vs Swift 1.5 Qwen3.8-27B
- Swift 1.5 Qwen3.8-27B vs Swift 1.5 Qwen3.8-Flash-Next
- Swift 1.5 Qwen3.8-27B vs ThinkingCap (Qwen3.8 27B)
- Swift 1.5 Qwen3.8-27B vs Baidu Unlimited-OCR-2
- Swift 1.5 Qwen3.8-27B vs WeVisOne-VL-2B
- Swift 1.5 Qwen3.8-27B vs WeVisOne-VL-4B
- Swift 1.5 Qwen3.8-27B vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/granite-4-2-8b-vs-swift-1-5-qwen3-8-27b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/granite-4-2-8b.json·/api/v1/tools/swift-1-5-qwen3-8-27b.json - From a terminal
anchor compare granite-4-2-8b swift-1-5-qwen3-8-27b(the CLI) - Over MCP
compare_tools {"a": "granite-4-2-8b", "b": "swift-1-5-qwen3-8-27b"}at/mcp, no key