Devstral 2 vs GLM-5.1 vs Codestral — Which Open Coding Model Wins?
The open-weight coding model space has matured significantly in 2026. Two models stand at the top for different reasons: Devstral 2 from Mistral for agentic coding and GLM-5.1 from Z.ai for marathon autonomous sessions.
This comparison focuses primarily on Devstral 2 versus GLM-5.1 since they compete most directly for the same use cases. For a broader view of the coding model landscape, see our AI model comparison.
Head-to-head comparison
| Devstral 2 | GLM-5.1 | |
|---|---|---|
| Purpose | Agentic coding | Long-horizon autonomous coding |
| Parameters | 123B dense | 754B MoE (40B active) |
| Context window | 256K | 200K |
| SWE-bench Verified | 72.2% | — |
| SWE-bench Pro | — | 58.4% |
| License | Modified MIT | MIT |
| Self-host requirements | 1x H100 | 4x A100 |
| Training hardware | NVIDIA | Huawei Ascend |
| API pricing | Moderate | $18/mo Coding Plan |
| Best for | Complex refactors | 8-hour autonomous sessions |
Note that SWE-bench Verified and SWE-bench Pro are different benchmarks with different methodologies, so scores are not directly comparable.
Devstral 2 — the agentic coding specialist
Devstral 2 is Mistral’s dedicated coding model built on a 123B dense architecture. Every parameter activates for every token, giving it consistent and predictable behavior across different coding tasks.
The 72.2% score on SWE-bench Verified places it among the top open-weight models for real-world software engineering.
The 256K context window is a practical advantage for large codebases. You can feed entire module directories, test suites, and documentation into a single prompt without hitting limits.
Devstral 2 excels at complex, multi-step coding tasks:
- Multi-file refactoring
- Feature implementation from specifications
- Code review with actionable suggestions
- Architectural analysis and dependency reasoning
Self-hosting requires a single H100 GPU, achievable for many organizations. The Modified MIT license allows commercial use with some restrictions.
For more options you can run on your own hardware, see our guide to the best AI models for coding locally in 2026.
GLM-5.1 — the marathon runner
GLM-5.1 takes a fundamentally different approach. Built on a 754B parameter MoE architecture with 40B active parameters, it was designed for long-horizon autonomous coding.
The headline feature is working independently for up to 8 hours on a single task.
This changes the workflow entirely. Instead of iterating back and forth with the model, you describe what you need, hand it off, and return hours later to find the work completed, tested, and documented.
The 58.4% score on SWE-bench Pro demonstrates strong performance on professional-grade software engineering tasks. GLM handles complex architectural decisions, subtle bug detection, and nuanced code review effectively.
GLM was trained on Huawei Ascend chips, making it one of the few frontier models not dependent on NVIDIA hardware.
Self-hosting requires 4x A100 GPUs — a higher bar than Devstral 2 but within reach for organizations with GPU infrastructure.
The Z.ai Coding Plan at $18/month is remarkably affordable for a model of this caliber. It provides API access compatible with Claude Code and other developer tools.
Architecture trade-offs
The dense versus MoE distinction has practical implications beyond benchmarks.
Devstral 2’s dense architecture means every token gets the full attention of all 123B parameters. This produces more consistent behavior and makes debugging easier since the reasoning path is more predictable.
GLM-5.1’s MoE architecture routes tokens to different expert networks. Different types of code may activate different internal pathways. This can occasionally produce inconsistent behavior on similar inputs, but also allows specialized experts for different programming languages and paradigms.
For tasks requiring high consistency — like applying the same refactoring pattern across hundreds of files — Devstral 2’s dense architecture may produce more uniform results.
For tasks requiring broad knowledge across many domains — like a full-stack feature touching frontend, backend, database, and infrastructure — GLM’s specialized experts may provide deeper domain-specific knowledge.
Practical setup recommendations
The ideal setup uses both models for their respective strengths.
Use Devstral 2 for interactive coding sessions where you need fast, high-quality responses to specific coding questions and refactoring tasks.
Use GLM-5.1 for longer autonomous tasks where you can hand off a complex feature and let the model work independently.
For budget-conscious developers, the GLM Coding Plan at $18/month is hard to beat. Pair it with a self-hosted Devstral 2 instance for interactive work, and you have a powerful coding setup at minimal cost.
Teams needing everything on-premises can self-host both, though the combined GPU requirements (1x H100 plus 4x A100) represent a significant infrastructure investment.
When to pick which
| Scenario | Recommended model |
|---|---|
| Interactive coding sessions | Devstral 2 |
| 8-hour autonomous tasks | GLM-5.1 |
| Multi-file refactoring | Devstral 2 |
| Full feature implementation | GLM-5.1 |
| Budget priority | GLM-5.1 ($18/mo plan) |
| Self-hosting simplicity | Devstral 2 (1x H100) |
| Open license priority | GLM-5.1 (MIT) |
FAQ
Is Devstral 2 better than GLM 5.1?
They excel at different things. Devstral 2 scores 72.2% on SWE-bench Verified and is stronger for interactive, multi-step coding tasks. GLM-5.1 leads on SWE-bench Pro (58.4%) and offers unique 8-hour autonomous capability. For quick refactoring and code review, Devstral 2 is better. For long autonomous sessions, GLM-5.1 wins.
Are both open source?
Both offer open weights under different licenses. GLM-5.1 uses the MIT license, one of the most permissive open-source licenses available. Devstral 2 uses a Modified MIT license allowing commercial use with some restrictions. Both can be downloaded and self-hosted, but GLM-5.1’s license is more permissive for commercial deployment.
Which is better for coding?
Both are excellent coding models serving different workflows. Devstral 2 is better for interactive coding — asking questions, getting refactoring suggestions, working through problems step by step. GLM-5.1 is better for autonomous coding — handing off a complex task and letting the model work independently for hours. Many developers use both.
Can I run both locally?
Yes, but hardware requirements differ significantly. Devstral 2 requires a single H100 GPU (80GB VRAM), achievable for well-equipped developers or small teams. GLM-5.1 requires 4x A100 GPUs, typically meaning a dedicated server or cloud GPU instance. Both can also be accessed via API if self-hosting is not practical.