Head to head · Open-weight models you run yourself · October 2026 research run
Devstral Small 2 vs GLM-4.7-Flash
GLM-4.7-Flash scores 63 (B) on agent readiness against Devstral Small 2's 61 (C), and leads in 1 of 4 scored categories. Devstral Small 2 leads on reach, risk 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 80
- Risk, 60 against 55
- Documentation and upkeep, 46 against 28
Watch for
No limitations, safety results, languages or data on the card
Best for Agents and developers who want a small, permissively licensed tool-calling model on one 24 to 32 GB GPU or Mac, in almost any runtime
Ahead on
- Agent interface, 85 against 65
Watch for
No change to the files since 29 January 2026 and no zai-org reply on discussions since 31 January
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.
Agent interface GLM-4.7-Flash +20
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.
GLM-4.7-Flash 85
A Jinja chat template sits in the repository, for 20. The tool-calling format is the full 25, because Z.ai itself names the glm47 parser for vLLM and SGLang, says tools are declared in OpenAI style and shows calls coming back as tool_calls. The template takes several calls in one turn, for 20. Structured output is 0, with nothing on the card and only the hosted API documented for JSON. Reasoning control is 10, thinking turned off per request with enable_thinking and the reasoning returned by the glm45 parser. Context is 10, 202,752 positions native with no rope scaling.
Documentation and upkeep Devstral Small 2 +18
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.
GLM-4.7-Flash 28
The card covers architecture, parameters and languages and misses four of the seven items, for 8. Training data is 0, as the card describes none. The last change was on 1 February 2026 at the latest, within a year but past 180 days, for 5. Responsiveness is 5, since zai-org answered 6 of the 40 most recent discussions, all by 31 January, which is a few. Version policy is 5 for a first release with no files replaced, and file health is 5, as config.json and tokenizer_config.json disagree on context and a config report waits without a reply.
Reach Devstral Small 2 +10
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.
GLM-4.7-Flash 80
Memory earns 25, since Ollama's smallest build is the 19 GB q4_K_M one, about 22.8 GB with the fifth added, which fits 24 GB. Runtimes reach the cap of 25, with model code in released vLLM, SGLang and Transformers, a converter in a released llama.cpp build and a local entry in Ollama's library. Builds earn nothing, because Z.ai publishes only BF16 and every quantised build is someone else's. The MIT licence with nothing added gives the full 15 for terms, the files download with no account or gate for 10, and Z.ai plus three other providers serve it for 5.
Risk Devstral Small 2 +5
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.
GLM-4.7-Flash 55
File safety is full, with safetensors only and the card loading it through Transformers' own code without remote code. Provenance is full, files in the zai-org organisation Z.ai links from GitHub with a SHA-256 for every shard. Safety evaluation and agent guidance are 0, as nothing is published on either. Licence risk is the full 15, since MIT adds no policy, threshold or changeable terms, though the repository carries no LICENSE file. The security programme is 0, with no security.txt, no SECURITY file and no advisories found.
Score by category
| Category | Weight this run | Devstral Small 2 | GLM-4.7-Flash | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 90 | 80 | Devstral Small 2 +10 |
| Risk | 20%20 | 60 | 55 | Devstral Small 2 +5 |
| Agent interface | 10%10 | 65 | 85 | GLM-4.7-Flash +20 |
| Documentation and upkeep | 10%10 | 46 | 28 | Devstral Small 2 +18 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | B 63/100 |
Facts side by side
| Fact | Devstral Small 2 | GLM-4.7-Flash |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Mistral AI | Z.ai (Zhipu AI) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | MIT |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-07-15 | 2026-01-29 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 2.8/5 (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.
GLM-4.7-Flash
A small MIT-licensed mixture-of-experts model with about 3B active parameters, for local agent and coding work with several tool calls per turn and optional thinking. It runs in Ollama at about 23 GB and in nearly every common runtime. The main risk is neglect, with no changes or replies since early February and no safety results.
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
GLM-4.7-Flash
- On our reference machine start it with
vllm serve zai-org/GLM-4.7-Flash --max-model-len 65536 --tool-call-parser glm47 --reasoning-parser glm45 --enable-auto-tool-choice --served-model-name glm-4.7-flash - Or run
ollama run glm-4.7-flashfor the 19 GB q4_K_M build - Without the glm47 and glm45 parsers, tool calls and reasoning come back as plain text
- Use a released vLLM (0.32.0 or later) or SGLang (0.5.22 or later); the card's nightly and pull-request builds date from January 2026
- Pass
chat_template_kwargs: {"enable_thinking": false}for short tasks, since thinking is on by default - Set
clear_thinking: falseto keep earlier reasoning in multi-turn agent work, as Z.ai does for τ²-Bench and Terminal Bench - Set the context length yourself, since config.json allows 202,752 tokens and tokenizer_config.json says 128,000
Questions
Which is better for AI agents, Devstral Small 2 or GLM-4.7-Flash?
GLM-4.7-Flash scores 63 (B) on agent readiness against Devstral Small 2's 61 (C), and leads in 1 of 4 scored categories. Devstral Small 2 leads on reach, risk and documentation and upkeep.
Other comparisons with Devstral Small 2 or GLM-4.7-Flash
- Agents-A1-4B vs Devstral Small 2
- Agents-A1-4B vs GLM-4.7-Flash
- Apertus v1.5 8B vs Devstral Small 2
- Apertus v1.5 8B vs GLM-4.7-Flash
- Apodex-1.1-mini vs Devstral Small 2
- Apodex-1.1-mini vs GLM-4.7-Flash
- Arete-27B vs Devstral Small 2
- Arete-27B vs GLM-4.7-Flash
- BigBang-v1 vs Devstral Small 2
- BigBang-v1 vs GLM-4.7-Flash
- Carbon-X vs Devstral Small 2
- Carbon-X vs GLM-4.7-Flash
- Cola2 vs Devstral Small 2
- Cola2 vs GLM-4.7-Flash
- Cosmos-Reason2-2B vs Devstral Small 2
- Cosmos-Reason2-2B vs GLM-4.7-Flash
- Darwin-36B-Opus vs Devstral Small 2
- Darwin-36B-Opus vs GLM-4.7-Flash
- DeepSeek-V3.2 vs Devstral Small 2
- DeepSeek-V3.2 vs GLM-4.7-Flash
- DeepSeek V4.1 Flash vs Devstral Small 2
- DeepSeek V4.1 Flash vs GLM-4.7-Flash
- DeepSeek-V4-Flash-0731 vs Devstral Small 2
- DeepSeek-V4-Flash-0731 vs GLM-4.7-Flash
- 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-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 ThinkingCap (Qwen3.8 27B)
- 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 GLM-4.7-Flash
- EXAONE 4.5 33B vs GLM-4.7-Flash
- Gemma 4 12B IT vs GLM-4.7-Flash
- Gemma 4 26B A4B IT vs GLM-4.7-Flash
- Gemma 4 31B IT vs GLM-4.7-Flash
- Gemma 4 E2B IT vs GLM-4.7-Flash
- Gemma 4 E4B IT vs GLM-4.7-Flash
- GLM-4.5-Air vs GLM-4.7-Flash
- GLM-4.7-Flash vs GLM-5.3
- GLM-4.7-Flash vs GLM-5.3-Flash
- GLM-4.7-Flash vs gpt-oss-120b
- GLM-4.7-Flash vs gpt-oss-20b
- GLM-4.7-Flash vs Granite 4.2 30B
- GLM-4.7-Flash vs Granite 4.2 3B
- GLM-4.7-Flash vs Granite 4.2 8B
- GLM-4.7-Flash vs Hy-Memory-30B-A3B
- GLM-4.7-Flash vs Hy3
- GLM-4.7-Flash vs Inkling
- GLM-4.7-Flash vs Inkling-Small
- GLM-4.7-Flash vs InternVL3.5-GPT-OSS-20B-A4B-Preview
- GLM-4.7-Flash vs Kimi K3
- GLM-4.7-Flash vs Kimi K3.1 Pro
- GLM-4.7-Flash vs Kolibri-1
- GLM-4.7-Flash vs Kronos
- GLM-4.7-Flash vs Laguna M.1
- GLM-4.7-Flash vs Laguna S 2.1
- GLM-4.7-Flash vs Laguna XS 2.1
- GLM-4.7-Flash vs LBS-27B
- GLM-4.7-Flash vs LFM2.5-2.6B
- GLM-4.7-Flash vs LFM3-1.7B
- GLM-4.7-Flash vs LFM3-Audio-2B
- GLM-4.7-Flash vs LFM3-Omni-2B
- GLM-4.7-Flash vs LFM3-VL-2B
- GLM-4.7-Flash vs Ling-3.0-tiny
- GLM-4.7-Flash vs Ling-3.1-flash-1019
- GLM-4.7-Flash vs LongCat-2.5
- GLM-4.7-Flash vs MiMo-V2.6 Distill Qwen 9B
- GLM-4.7-Flash vs MiMo-V2.6-Flash-RL
- GLM-4.7-Flash vs MiniCPM-o 4.5
- GLM-4.7-Flash vs MiniCPM-V 4.7 1B
- GLM-4.7-Flash vs MiniCPM-V 4.7 35B-A3B
- GLM-4.7-Flash vs MiniCPM5-2B
- GLM-4.7-Flash vs MiniMax-M2.5
- GLM-4.7-Flash vs MiniMax-M2.7
- GLM-4.7-Flash vs MiniMax-M3.1-Flash
- GLM-4.7-Flash vs Mistral Large 4
- GLM-4.7-Flash vs Muse Glimmer 30B
- GLM-4.7-Flash vs Nanbeige4.2-3B
- GLM-4.7-Flash vs Nanbeige4.3-33B
- GLM-4.7-Flash vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- GLM-4.7-Flash vs Nemotron 3.5 Lightning 30B A3B
- GLM-4.7-Flash vs Nemotron 3 Nano 30B A3B
- GLM-4.7-Flash vs Nemotron 3 Nano 4B
- GLM-4.7-Flash vs Nemotron 3 Nano Omni 30B A3B Reasoning
- GLM-4.7-Flash vs Nemotron 3 Super 120B A12B
- GLM-4.7-Flash vs Nemotron 3 Ultra 550B A55B
- GLM-4.7-Flash vs North Mini Code 1.0
- GLM-4.7-Flash vs occamy-1.0
- GLM-4.7-Flash vs Olmo 3 7B Think
- GLM-4.7-Flash vs Ornith-1.5-122B
- GLM-4.7-Flash vs Ornith-1.5-35B-A3B
- GLM-4.7-Flash vs Ornith-1.5-397B
- GLM-4.7-Flash vs Ornith-1.5-9B
- GLM-4.7-Flash vs OTel-2.0-LLM-31B-IT
- GLM-4.7-Flash vs Qwen-AgentWorld-35B-A3B
- GLM-4.7-Flash vs Qwen3-30B-A3B-Instruct-2507
- GLM-4.7-Flash vs Qwen3-4B-Instruct-2507
- GLM-4.7-Flash vs Qwen3.5-0.8B
- GLM-4.7-Flash vs Qwen3.5-122B-A10B
- GLM-4.7-Flash vs Qwen3.5-2B
- GLM-4.7-Flash vs Qwen3.5-397B-A17B
- GLM-4.7-Flash vs Qwen3.5-4B
- GLM-4.7-Flash vs Qwen3.5-9B
- GLM-4.7-Flash vs Qwen3.6-27B
- GLM-4.7-Flash vs Qwen3.6-35B-A3B
- GLM-4.7-Flash vs Qwen3.8-27B
- GLM-4.7-Flash vs Qwen3.8-Flash-Next
- GLM-4.7-Flash vs Qwen3-Coder-30B-A3B-Instruct
- GLM-4.7-Flash vs Qwen3-Coder-Next
- GLM-4.7-Flash vs Qwen3-Omni-30B-A3B-Instruct
- GLM-4.7-Flash vs Qwen3-VL-2B-Instruct
- GLM-4.7-Flash vs Qwen3-VL-30B-A3B-Instruct
- GLM-4.7-Flash vs Qwen3-VL-32B-Instruct
- GLM-4.7-Flash vs Qwen3-VL-4B-Instruct
- GLM-4.7-Flash vs Qwen3-VL-8B-Instruct
- GLM-4.7-Flash vs Qwopus3.8-27B-Flash-V2
- GLM-4.7-Flash vs Recursive Flint
- GLM-4.7-Flash vs Ripple-65B Preview
- GLM-4.7-Flash vs SenseNova-Loop-9B-Preview
- GLM-4.7-Flash vs SmolLM3-3B
- GLM-4.7-Flash vs Spark-X2.6-4B
- GLM-4.7-Flash vs Step 3.7 Flash
- GLM-4.7-Flash vs Swift 1.5 Qwen3.8-27B
- GLM-4.7-Flash vs Swift 1.5 Qwen3.8-Flash-Next
- GLM-4.7-Flash vs ThinkingCap (Qwen3.8 27B)
- GLM-4.7-Flash vs Baidu Unlimited-OCR-2
- GLM-4.7-Flash vs WeVisOne-VL-2B
- GLM-4.7-Flash vs WeVisOne-VL-4B
- GLM-4.7-Flash vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/devstral-small-2-vs-glm-4-7-flash.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/devstral-small-2.json·/api/v1/tools/glm-4-7-flash.json - From a terminal
anchor compare devstral-small-2 glm-4-7-flash(the CLI) - Over MCP
compare_tools {"a": "devstral-small-2", "b": "glm-4-7-flash"}at/mcp, no key