Head to head · Open-weight models you run yourself · October 2026 research run
Devstral Small 2 vs ThinkingCap (Qwen3.8 27B)
Devstral Small 2 and ThinkingCap (Qwen3.8 27B) score within a point of each other on agent readiness, 61 (C) and 61 (C). ThinkingCap (Qwen3.8 27B) leads on agent interface and documentation and upkeep. 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 Developers who want a dependable local coding agent on one consumer GPU or a 32 GB Mac
Ahead on
- Reach, 90 against 75
- Risk, 60 against 45
Watch for
No limitations, safety results, languages or data on the card
Best for Small teams and individuals running Qwen3.8-27B for agent or reasoning work who want shorter thinking traces on one 24 GB GPU or Mac.
Ahead on
- Agent interface, 70 against 65
- Documentation and upkeep, 53 against 46
Watch for
PolyForm Small Business licence, free commercially only below 100 people and 1,000,000 USD (2019) revenue
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.
Reach Devstral Small 2 +15
Devstral Small 2 90
Memory 25, the smallest build at 4 bits from a runtime's own library is Ollama's devstral-small-2 at Q4_K_M, 15 GB, and 15 GB × 1.2 = 18 GB, within 24 GB. Mistral's own FP8 files are 25.79 GB. Runtimes 25, Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 have the mistral3 model, llama.cpp b12242 has the architecture with a GGUF from its own organisation, and Ollama's library has devstral-small-2. Builds 10, one quantised precision from Mistral, the FP8 weights it releases. Terms 15, Apache 2.0 in the card's metadata and text, an OSI licence with nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, Mistral's own API serves Devstral Small 2: the card says the model is available through its API and points there when a local runtime underperforms. 25 + 25 + 10 + 15 + 10 + 5 = 90.
ThinkingCap (Qwen3.8 27B) 75
Memory 25 of 30, BottleCap's IQ4_XS GGUF is 15.5 GB, 18.6 GB with a fifth added, which fits 24 GB. Runtimes 20, it keeps the base's qwen3_5 architecture, which llama.cpp, vLLM, SGLang and Transformers run in released versions, while Ollama's entry is the base's. Builds 15, FP8, NVFP4, MLX and GGUF from BottleCap. Terms 10, PolyForm Small Business is free commercially only below 100 people and 1,000,000 USD (2019) revenue. Download 5, the main repository is gated behind a click-through with an account and contact details, though the GGUF, NVFP4 and MLX builds aren't. Hosted 0, no API or inference provider serves it.
Risk Devstral Small 2 +15
Devstral Small 2 60
File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, files in Mistral's verified mistralai organisation, which Mistral's site links to per our Mistral Large 4 listing (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on prompt injection, instruction priority or what the model refuses. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 5, mistral.ai's security.txt gives a security contact and a submission policy, as read for the Mistral Large 4 listing (5). 25 + 15 + 0 + 0 + 15 + 5 = 60.
ThinkingCap (Qwen3.8 27B) 45
File safety 25, safetensors only and no code in the repository. Provenance 10 of 15, the files are in BottleCap's verified organisation, but the Hub hides the gated files' SHA-256 from the public, so checksums couldn't be confirmed. Safety evaluation 0, none published. Agent guidance 0. Licence risk 10, 5 off for the size and revenue threshold. Security programme 0, no security.txt, disclosure policy or security contact.
Documentation and upkeep ThinkingCap (Qwen3.8 27B) +7
Devstral Small 2 46
Model card 11, Intended use, the architecture, the parameters and the context are on the card. Training data 0, Nothing on the card about the training data. Last change 10, the last commit, on 15 July 2026, updated the files to match mistral-common, 118 days before this check and within 180. Responsiveness 10, Of the 23 most recent discussions that aren't pull requests (9 December 2025 to 6 November 2026), 11 have a reply from a mistralai member, which is some. Version policy 5, Devstral Small 1.1 stays in its own repository, which this card links as the predecessor, with no dated note on the older card that we read. File health 10, Config, template, tokenizer and card agree, and the June and July commits fixed stray tokens and brought the files in line with mistral-common. 11 + 0 + 10 + 10 + 5 + 10 = 46.
ThinkingCap (Qwen3.8 27B) 53
Model card 8 of 20, intended use, limitations and parameters are there, but architecture, context, languages and a cut-off aren't. Training data 0, not described. Last change 20, BottleCap's GGUF build of the model changed its card on 7 October 2026, 27 days ago. Responsiveness 10, BottleCap answered 4 of 8 discussions on the main repository. Version policy 5, the earlier ThinkingCap stays in its own repository with an undated pointer to this one. File health 10, the readable metadata and builds agree and no broken-file report waits.
Agent interface ThinkingCap (Qwen3.8 27B) +5
Devstral Small 2 65
Chat template 20, chat_template.jinja in the repository (11,362 characters), with the tokenizer's tekken file for mistral-common. Tool-calling format 25, the card documents tools in the OpenAI function format with an example, names the mistral tool-call parser with auto tool choice for vLLM, shows the same for SGLang, and ships test prompts with the expected tool-call output. Trained tool calling 10, the card says the model excels at using tools, and its tests cover one tool call and calls made one at a time in sequence. Structured output 0, Nothing on the card about JSON or schema-constrained answers for this model. Reasoning control 0, no reasoning mode is documented, and a June 2026 commit removed stray thinking tokens from the files. Context 10, 256K tokens per the card, served at 262,144 in its vLLM command. 20 + 25 + 10 + 0 + 0 + 10 = 65.
ThinkingCap (Qwen3.8 27B) 70
Chat template 20, the same Jinja template as Qwen3.8-27B, in the repository. Tool-calling format 10 of 25, the card names the parsers but the format lives only in the template. Trained tool calling 20, several calls a turn, and BottleCap evaluated agent tasks. Structured output 0, not documented. Reasoning control 10, reasoning_effort budgets thinking per request and the card says where the reasoning comes back. Context 10, 262,144 tokens.
Score by category
| Category | Weight this run | Devstral Small 2 | ThinkingCap (Qwen3.8 27B) | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 90 | 75 | Devstral Small 2 +15 |
| Risk | 20%20 | 60 | 45 | Devstral Small 2 +15 |
| Agent interface | 10%10 | 65 | 70 | ThinkingCap (Qwen3.8 27B) +5 |
| Documentation and upkeep | 10%10 | 46 | 53 | ThinkingCap (Qwen3.8 27B) +7 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 61/100 |
Facts side by side
| Fact | Devstral Small 2 | ThinkingCap (Qwen3.8 27B) |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Mistral AI | BottleCap AI |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | PolyForm-Small-Business-1.0.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-07-15 | 2026-10-06 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | 2026-01-21 |
| Agent reviews | none | 2.5/5 (4) |
Verdicts
Devstral Small 2
The easy one to run: a 24B coding agent under Apache 2.0 that five runtimes carry, 15 GB at 4 bits in Ollama, with a 256K context, image input and well-documented tool calling. It's eleven months old, calls tools one at a time, has no reasoning mode, and its card says nothing on limits, safety or data.
ThinkingCap (Qwen3.8 27B)
A Qwen3.8-27B with shorter reasoning, for agent work where thinking tokens cost time, and BottleCap's 4-bit GGUF runs in about 19 GB. The main limits are the licence, which needs a paid licence above 100 people or 1,000,000 USD revenue, a gated main repository, and no safety results or training-data description.
Before you call either
Devstral Small 2
ollama run devstral-small-2is the quickest start, at 15 GB- On vLLM pass
--tool-call-parser mistral --enable-auto-tool-choice - Send the system prompt from CHAT_SYSTEM_PROMPT.txt in the repository. The card says results depend on it
- Use a low temperature. The repository's default is 0.15
- Plan for one tool call per turn
- The same model is on our Mistral API listing if you'd rather not host it
ThinkingCap (Qwen3.8 27B)
- Check the licence first, an organisation with 100 or more people or 1,000,000 USD (2019) revenue needs a commercial licence from BottleCap
- For files without the gate use
llama-server -hf bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF:Q4_K_M --jinja, about 17.4 GB - On vLLM use
--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_xml, and on SGLang--tool-call-parser qwen3_coder - Set
reasoning_effort(xhigh, medium or low) in chat_template_kwargs, any other value makes the template raise an error - Keep the base's sampling (temperature 1.0, top_p 0.95, top_k 20), the GGUF card says greedy decoding can loop
- The GGUFs carry an MTP head, and llama.cpp builds without MTP support for this architecture may refuse to load them with a missing blk.64 tensor
Questions
Which is better for AI agents, Devstral Small 2 or ThinkingCap (Qwen3.8 27B)?
Devstral Small 2 and ThinkingCap (Qwen3.8 27B) score within a point of each other on agent readiness, 61 (C) and 61 (C). ThinkingCap (Qwen3.8 27B) leads on agent interface and documentation and upkeep.
Other comparisons with Devstral Small 2 or ThinkingCap (Qwen3.8 27B)
- Agents-A1-4B vs Devstral Small 2
- Agents-A1-4B vs ThinkingCap (Qwen3.8 27B)
- Apertus v1.5 8B vs Devstral Small 2
- Apertus v1.5 8B vs ThinkingCap (Qwen3.8 27B)
- Apodex-1.1-mini vs Devstral Small 2
- Apodex-1.1-mini vs ThinkingCap (Qwen3.8 27B)
- Arete-27B vs Devstral Small 2
- Arete-27B vs ThinkingCap (Qwen3.8 27B)
- BigBang-v1 vs Devstral Small 2
- BigBang-v1 vs ThinkingCap (Qwen3.8 27B)
- Carbon-X vs Devstral Small 2
- Carbon-X vs ThinkingCap (Qwen3.8 27B)
- Cola2 vs Devstral Small 2
- Cola2 vs ThinkingCap (Qwen3.8 27B)
- Cosmos-Reason2-2B vs Devstral Small 2
- Cosmos-Reason2-2B vs ThinkingCap (Qwen3.8 27B)
- Darwin-36B-Opus vs Devstral Small 2
- Darwin-36B-Opus vs ThinkingCap (Qwen3.8 27B)
- DeepSeek-V3.2 vs Devstral Small 2
- DeepSeek-V3.2 vs ThinkingCap (Qwen3.8 27B)
- DeepSeek V4.1 Flash vs Devstral Small 2
- DeepSeek V4.1 Flash vs ThinkingCap (Qwen3.8 27B)
- DeepSeek-V4-Flash-0731 vs Devstral Small 2
- DeepSeek-V4-Flash-0731 vs ThinkingCap (Qwen3.8 27B)
- Devstral Small 2 vs DiffusionGemma 26B A4B IT
- Devstral Small 2 vs EXAONE 4.5 33B
- Devstral Small 2 vs Gemma 4 12B IT
- Devstral Small 2 vs Gemma 4 26B A4B IT
- Devstral Small 2 vs Gemma 4 31B IT
- Devstral Small 2 vs Gemma 4 E2B IT
- Devstral Small 2 vs Gemma 4 E4B IT
- Devstral Small 2 vs GLM-4.5-Air
- Devstral Small 2 vs GLM-4.7-Flash
- Devstral Small 2 vs GLM-5.3
- Devstral Small 2 vs GLM-5.3-Flash
- Devstral Small 2 vs gpt-oss-120b
- Devstral Small 2 vs gpt-oss-20b
- Devstral Small 2 vs Granite 4.2 30B
- Devstral Small 2 vs Granite 4.2 3B
- Devstral Small 2 vs Granite 4.2 8B
- Devstral Small 2 vs Hy-Memory-30B-A3B
- Devstral Small 2 vs Hy3
- Devstral Small 2 vs Inkling
- Devstral Small 2 vs Inkling-Small
- Devstral Small 2 vs InternVL3.5-GPT-OSS-20B-A4B-Preview
- Devstral Small 2 vs Kimi K3
- Devstral Small 2 vs Kimi K3.1 Pro
- Devstral Small 2 vs Kolibri-1
- Devstral Small 2 vs Kronos
- Devstral Small 2 vs Laguna M.1
- Devstral Small 2 vs Laguna S 2.1
- Devstral Small 2 vs Laguna XS 2.1
- Devstral Small 2 vs LBS-27B
- Devstral Small 2 vs LFM2.5-2.6B
- Devstral Small 2 vs LFM3-1.7B
- Devstral Small 2 vs LFM3-Audio-2B
- Devstral Small 2 vs LFM3-Omni-2B
- Devstral Small 2 vs LFM3-VL-2B
- Devstral Small 2 vs Ling-3.0-tiny
- Devstral Small 2 vs Ling-3.1-flash-1019
- Devstral Small 2 vs LongCat-2.5
- Devstral Small 2 vs MiMo-V2.6 Distill Qwen 9B
- Devstral Small 2 vs MiMo-V2.6-Flash-RL
- Devstral Small 2 vs MiniCPM-o 4.5
- Devstral Small 2 vs MiniCPM-V 4.7 1B
- Devstral Small 2 vs MiniCPM-V 4.7 35B-A3B
- Devstral Small 2 vs MiniCPM5-2B
- Devstral Small 2 vs MiniMax-M2.5
- Devstral Small 2 vs MiniMax-M2.7
- Devstral Small 2 vs MiniMax-M3.1-Flash
- Devstral Small 2 vs Mistral Large 4
- Devstral Small 2 vs Muse Glimmer 30B
- Devstral Small 2 vs Nanbeige4.2-3B
- Devstral Small 2 vs Nanbeige4.3-33B
- Devstral Small 2 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- Devstral Small 2 vs Nemotron 3.5 Lightning 30B A3B
- Devstral Small 2 vs Nemotron 3 Nano 30B A3B
- Devstral Small 2 vs Nemotron 3 Nano 4B
- Devstral Small 2 vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Devstral Small 2 vs Nemotron 3 Super 120B A12B
- Devstral Small 2 vs Nemotron 3 Ultra 550B A55B
- Devstral Small 2 vs North Mini Code 1.0
- Devstral Small 2 vs occamy-1.0
- Devstral Small 2 vs Olmo 3 7B Think
- Devstral Small 2 vs Ornith-1.5-122B
- Devstral Small 2 vs Ornith-1.5-35B-A3B
- Devstral Small 2 vs Ornith-1.5-397B
- Devstral Small 2 vs Ornith-1.5-9B
- Devstral Small 2 vs OTel-2.0-LLM-31B-IT
- Devstral Small 2 vs Qwen-AgentWorld-35B-A3B
- Devstral Small 2 vs Qwen3-30B-A3B-Instruct-2507
- Devstral Small 2 vs Qwen3-4B-Instruct-2507
- Devstral Small 2 vs Qwen3.5-0.8B
- Devstral Small 2 vs Qwen3.5-122B-A10B
- Devstral Small 2 vs Qwen3.5-2B
- Devstral Small 2 vs Qwen3.5-397B-A17B
- Devstral Small 2 vs Qwen3.5-4B
- Devstral Small 2 vs Qwen3.5-9B
- Devstral Small 2 vs Qwen3.6-27B
- Devstral Small 2 vs Qwen3.6-35B-A3B
- Devstral Small 2 vs Qwen3.8-27B
- Devstral Small 2 vs Qwen3.8-Flash-Next
- Devstral Small 2 vs Qwen3-Coder-30B-A3B-Instruct
- Devstral Small 2 vs Qwen3-Coder-Next
- Devstral Small 2 vs Qwen3-Omni-30B-A3B-Instruct
- Devstral Small 2 vs Qwen3-VL-2B-Instruct
- Devstral Small 2 vs Qwen3-VL-30B-A3B-Instruct
- Devstral Small 2 vs Qwen3-VL-32B-Instruct
- Devstral Small 2 vs Qwen3-VL-4B-Instruct
- Devstral Small 2 vs Qwen3-VL-8B-Instruct
- Devstral Small 2 vs Qwopus3.8-27B-Flash-V2
- Devstral Small 2 vs Recursive Flint
- Devstral Small 2 vs Ripple-65B Preview
- Devstral Small 2 vs SenseNova-Loop-9B-Preview
- Devstral Small 2 vs SmolLM3-3B
- Devstral Small 2 vs Spark-X2.6-4B
- Devstral Small 2 vs Step 3.7 Flash
- Devstral Small 2 vs Swift 1.5 Qwen3.8-27B
- Devstral Small 2 vs Swift 1.5 Qwen3.8-Flash-Next
- Devstral Small 2 vs Baidu Unlimited-OCR-2
- Devstral Small 2 vs WeVisOne-VL-2B
- Devstral Small 2 vs WeVisOne-VL-4B
- Devstral Small 2 vs ZAYA1-8B
- DiffusionGemma 26B A4B IT vs ThinkingCap (Qwen3.8 27B)
- EXAONE 4.5 33B vs ThinkingCap (Qwen3.8 27B)
- Gemma 4 12B IT vs ThinkingCap (Qwen3.8 27B)
- Gemma 4 26B A4B IT vs ThinkingCap (Qwen3.8 27B)
- Gemma 4 31B IT vs ThinkingCap (Qwen3.8 27B)
- Gemma 4 E2B IT vs ThinkingCap (Qwen3.8 27B)
- Gemma 4 E4B IT vs ThinkingCap (Qwen3.8 27B)
- GLM-4.5-Air vs ThinkingCap (Qwen3.8 27B)
- GLM-4.7-Flash vs ThinkingCap (Qwen3.8 27B)
- GLM-5.3-Flash vs ThinkingCap (Qwen3.8 27B)
- GLM-5.3 vs ThinkingCap (Qwen3.8 27B)
- gpt-oss-120b vs ThinkingCap (Qwen3.8 27B)
- gpt-oss-20b vs ThinkingCap (Qwen3.8 27B)
- Granite 4.2 30B vs ThinkingCap (Qwen3.8 27B)
- Granite 4.2 3B vs ThinkingCap (Qwen3.8 27B)
- Granite 4.2 8B vs ThinkingCap (Qwen3.8 27B)
- Hy-Memory-30B-A3B vs ThinkingCap (Qwen3.8 27B)
- Hy3 vs ThinkingCap (Qwen3.8 27B)
- Inkling-Small vs ThinkingCap (Qwen3.8 27B)
- Inkling vs ThinkingCap (Qwen3.8 27B)
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs ThinkingCap (Qwen3.8 27B)
- Kimi K3.1 Pro vs ThinkingCap (Qwen3.8 27B)
- Kimi K3 vs ThinkingCap (Qwen3.8 27B)
- Kolibri-1 vs ThinkingCap (Qwen3.8 27B)
- Kronos vs ThinkingCap (Qwen3.8 27B)
- Laguna M.1 vs ThinkingCap (Qwen3.8 27B)
- Laguna S 2.1 vs ThinkingCap (Qwen3.8 27B)
- Laguna XS 2.1 vs ThinkingCap (Qwen3.8 27B)
- LBS-27B vs ThinkingCap (Qwen3.8 27B)
- LFM2.5-2.6B vs ThinkingCap (Qwen3.8 27B)
- LFM3-1.7B vs ThinkingCap (Qwen3.8 27B)
- LFM3-Audio-2B vs ThinkingCap (Qwen3.8 27B)
- LFM3-Omni-2B vs ThinkingCap (Qwen3.8 27B)
- LFM3-VL-2B vs ThinkingCap (Qwen3.8 27B)
- Ling-3.0-tiny vs ThinkingCap (Qwen3.8 27B)
- Ling-3.1-flash-1019 vs ThinkingCap (Qwen3.8 27B)
- LongCat-2.5 vs ThinkingCap (Qwen3.8 27B)
- MiMo-V2.6 Distill Qwen 9B vs ThinkingCap (Qwen3.8 27B)
- MiMo-V2.6-Flash-RL vs ThinkingCap (Qwen3.8 27B)
- MiniCPM-o 4.5 vs ThinkingCap (Qwen3.8 27B)
- MiniCPM-V 4.7 1B vs ThinkingCap (Qwen3.8 27B)
- MiniCPM-V 4.7 35B-A3B vs ThinkingCap (Qwen3.8 27B)
- MiniCPM5-2B vs ThinkingCap (Qwen3.8 27B)
- MiniMax-M2.5 vs ThinkingCap (Qwen3.8 27B)
- MiniMax-M2.7 vs ThinkingCap (Qwen3.8 27B)
- MiniMax-M3.1-Flash vs ThinkingCap (Qwen3.8 27B)
- Mistral Large 4 vs ThinkingCap (Qwen3.8 27B)
- Muse Glimmer 30B vs ThinkingCap (Qwen3.8 27B)
- Nanbeige4.2-3B vs ThinkingCap (Qwen3.8 27B)
- Nanbeige4.3-33B vs ThinkingCap (Qwen3.8 27B)
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs ThinkingCap (Qwen3.8 27B)
- Nemotron 3.5 Lightning 30B A3B vs ThinkingCap (Qwen3.8 27B)
- Nemotron 3 Nano 30B A3B vs ThinkingCap (Qwen3.8 27B)
- Nemotron 3 Nano 4B vs ThinkingCap (Qwen3.8 27B)
- Nemotron 3 Nano Omni 30B A3B Reasoning vs ThinkingCap (Qwen3.8 27B)
- Nemotron 3 Super 120B A12B vs ThinkingCap (Qwen3.8 27B)
- Nemotron 3 Ultra 550B A55B vs ThinkingCap (Qwen3.8 27B)
- North Mini Code 1.0 vs ThinkingCap (Qwen3.8 27B)
- occamy-1.0 vs ThinkingCap (Qwen3.8 27B)
- Olmo 3 7B Think vs ThinkingCap (Qwen3.8 27B)
- Ornith-1.5-122B vs ThinkingCap (Qwen3.8 27B)
- Ornith-1.5-35B-A3B vs ThinkingCap (Qwen3.8 27B)
- Ornith-1.5-397B vs ThinkingCap (Qwen3.8 27B)
- Ornith-1.5-9B vs ThinkingCap (Qwen3.8 27B)
- OTel-2.0-LLM-31B-IT vs ThinkingCap (Qwen3.8 27B)
- Qwen-AgentWorld-35B-A3B vs ThinkingCap (Qwen3.8 27B)
- Qwen3-30B-A3B-Instruct-2507 vs ThinkingCap (Qwen3.8 27B)
- Qwen3-4B-Instruct-2507 vs ThinkingCap (Qwen3.8 27B)
- Qwen3.5-0.8B vs ThinkingCap (Qwen3.8 27B)
- Qwen3.5-122B-A10B vs ThinkingCap (Qwen3.8 27B)
- Qwen3.5-2B vs ThinkingCap (Qwen3.8 27B)
- Qwen3.5-397B-A17B vs ThinkingCap (Qwen3.8 27B)
- Qwen3.5-4B vs ThinkingCap (Qwen3.8 27B)
- Qwen3.5-9B vs ThinkingCap (Qwen3.8 27B)
- Qwen3.6-27B vs ThinkingCap (Qwen3.8 27B)
- Qwen3.6-35B-A3B vs ThinkingCap (Qwen3.8 27B)
- Qwen3.8-27B vs ThinkingCap (Qwen3.8 27B)
- Qwen3.8-Flash-Next vs ThinkingCap (Qwen3.8 27B)
- Qwen3-Coder-30B-A3B-Instruct vs ThinkingCap (Qwen3.8 27B)
- Qwen3-Coder-Next vs ThinkingCap (Qwen3.8 27B)
- Qwen3-Omni-30B-A3B-Instruct vs ThinkingCap (Qwen3.8 27B)
- Qwen3-VL-2B-Instruct vs ThinkingCap (Qwen3.8 27B)
- Qwen3-VL-30B-A3B-Instruct vs ThinkingCap (Qwen3.8 27B)
- Qwen3-VL-32B-Instruct vs ThinkingCap (Qwen3.8 27B)
- Qwen3-VL-4B-Instruct vs ThinkingCap (Qwen3.8 27B)
- Qwen3-VL-8B-Instruct vs ThinkingCap (Qwen3.8 27B)
- Qwopus3.8-27B-Flash-V2 vs ThinkingCap (Qwen3.8 27B)
- Recursive Flint vs ThinkingCap (Qwen3.8 27B)
- Ripple-65B Preview vs ThinkingCap (Qwen3.8 27B)
- SenseNova-Loop-9B-Preview vs ThinkingCap (Qwen3.8 27B)
- SmolLM3-3B vs ThinkingCap (Qwen3.8 27B)
- Spark-X2.6-4B vs ThinkingCap (Qwen3.8 27B)
- Step 3.7 Flash vs ThinkingCap (Qwen3.8 27B)
- Swift 1.5 Qwen3.8-27B vs ThinkingCap (Qwen3.8 27B)
- Swift 1.5 Qwen3.8-Flash-Next vs ThinkingCap (Qwen3.8 27B)
- ThinkingCap (Qwen3.8 27B) vs Baidu Unlimited-OCR-2
- ThinkingCap (Qwen3.8 27B) vs WeVisOne-VL-2B
- ThinkingCap (Qwen3.8 27B) vs WeVisOne-VL-4B
- ThinkingCap (Qwen3.8 27B) vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/devstral-small-2-vs-thinkingcap-qwen3-8-27b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/devstral-small-2.json·/api/v1/tools/thinkingcap-qwen3-8-27b.json - From a terminal
anchor compare devstral-small-2 thinkingcap-qwen3-8-27b(the CLI) - Over MCP
compare_tools {"a": "devstral-small-2", "b": "thinkingcap-qwen3-8-27b"}at/mcp, no key