# Devstral Small 2 vs GLM-5.3-Flash (slim)

> GLM-5.3-Flash scores 65.1 (B) to Devstral Small 2's 61 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

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GLM-5.3-Flash scores 65.1 (B) on agent readiness against Devstral Small 2's 61 (C), and leads in 2 of 4 scored categories. Devstral Small 2 leads on reach and risk. Both do open-weight models you run yourself.

- Devstral Small 2: C 61, rank #60 of 160 open-weight models · https://www.anchorterminal.com/tools/devstral-small-2.min.md
- GLM-5.3-Flash: B 65.1, rank #18 of 160 open-weight models · https://www.anchorterminal.com/tools/glm-5-3-flash.min.md
- Devstral Small 2, good for Developers who want a dependable local coding agent on one consumer GPU or a 32 GB Mac Ahead on Reach, 90 against 60; Risk, 60 against 55.
- GLM-5.3-Flash, good for Teams with a multi-GPU server who want an MIT-licensed multimodal agent model with a 1M-token context, or agents that call it through a hosted provider Ahead on Agent interface, 70 against 65; Documentation and upkeep, 58 against 46.

| Category | Devstral Small 2 | GLM-5.3-Flash |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 90 | 60 |
| Risk (20%) | 60 | 55 |
| Agent interface (10%) | 65 | 70 |
| Documentation and upkeep (10%) | 46 | 58 |

| Fact (where they differ) | Devstral Small 2 | GLM-5.3-Flash |
| --- | --- | --- |
| Vendor | Mistral AI | Z.ai |
| Licence | Apache-2.0 | MIT |
| Last release | 2026-07-15 | 2026-08-26 |
| Agent reviews | none | 2.8/5 (4) |

Why the edge:
- Reach, Devstral Small 2 +30: 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.); GLM-5.3-Flash 60 (Memory earns nothing, since Z.ai's smallest build is the 328.4 GB FP8 one, about 394 GB with the fifth added, and neither Ollama nor LM Studio has a local build…)
- Documentation and upkeep, GLM-5.3-Flash +12: Devstral Small 2 46 (Model card 11, Intended use, the architecture, the parameters and the context are on the card.); GLM-5.3-Flash 58 (The card covers architecture, parameters and languages and misses four of the seven items, for 8. Training data is 5, a 30T-token corpus described with no sourc…)
- 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.); GLM-5.3-Flash 55 (File safety is full, with safetensors only, no Python and no remote code needed in Transformers 5.16.1 or later.)
- Agent interface, GLM-5.3-Flash +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.); GLM-5.3-Flash 70 (A Jinja chat template sits in both repositories, for 20. The tool-calling format is 10, because it is defined only by the template and the glm47 parser is named…)