Model comparison
Devstral Small 2505 vs GLM-5.2
GLM-5.2 is the stronger model overall, scoring 51.1 to 34.3 on the Noometry Index. Devstral Small 2505 costs 14× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.
Last verified . 3 shared benchmarks.
Mistral AISummary
- They share 3 benchmarks with published results for both. Devstral Small 2505 scores higher in 0 categories and GLM-5.2 in 2 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5.2 leads 42.3 to 19.7.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 37.7% for Devstral Small 2505 and 62.6% for GLM-5.2.
- Devstral Small 2505 is cheaper at $0.10 / $0.30 per million input/output tokens, against $1.40 / $4.40 for GLM-5.2.
- GLM-5.2 accepts more context: 1M tokens versus 128K.
Side by side
| Devstral Small 2505 | GLM-5.2 | |
|---|---|---|
| Provider | Mistral AI | Z.ai (Zhipu) |
| Noometry Index | 34.3 | 51.1 |
| Released | 2025-05-07 | 2026-06-13 |
| Weights | Open | Open |
| Context window | 128K | 1M |
| Max output | 128K | 131K |
| Input $ / M tokens | $0.10 | $1.40 |
| Output $ / M tokens | $0.30 | $4.40 |
| Results tracked | 4 | 51 |
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Category by category
Coding GLM-5.2 leads
Devstral Small 2505: 38.9 (#166), GLM-5.2: 51.3 (#41)
| Benchmark | Devstral Small 2505 | GLM-5.2 |
|---|---|---|
| SciCode | 28.8% | 50.5% |
| SWE-bench Verified | — | 78.7% |
| DeepSWE | — | 43.8% |
| FrontierCode | — | 24.5% |
| SWE-bench Verified (bash only) | 56.4% | — |
| LMArena WebDev | — | 1603 |
| WeirdML | — | 70.1% |
| LMArena Coding | — | 1485 |
| ALE-Bench | — | 1,047 |
Agentic & Tool Use Not comparable
Devstral Small 2505: —, GLM-5.2: 32.4 (#63)
| Benchmark | Devstral Small 2505 | GLM-5.2 |
|---|---|---|
| APEX-Agents | — | 45.2% |
| τ²-bench Banking | — | 37.1% |
| PostTrainBench | — | 31.7% |
| GBAEval | — | 0% |
| Vending-Bench 2 | — | 8,314 |
Reasoning GLM-5.2 leads
Devstral Small 2505: 19.7 (#252), GLM-5.2: 42.3 (#52)
| Benchmark | Devstral Small 2505 | GLM-5.2 |
|---|---|---|
| Kagi LLM Benchmark | 37.7% | 62.6% |
| CritPt | 0% | 20.9% |
| ARC-AGI-2 | — | 22.8% |
| SimpleBench | — | 58.8% |
| NYT Connections (extended) | — | 74.3% |
| ARC-AGI-1 | — | 77% |
| Chess Puzzles | — | 21% |
| EBR-Bench | — | 9.5% |
| LMArena Hard Prompts | — | 1480 |
| Mystery Game Puzzles | — | 19% |
| DTBench | — | 93.6% |
| LMCA | — | 45.8% |
| Surface Evolver Bench | — | 55.6% |
| Epoch Capabilities Index | — | 151.78 |
Math Not comparable
Devstral Small 2505: —, GLM-5.2: 55.7 (#43)
| Benchmark | Devstral Small 2505 | GLM-5.2 |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 59.2% |
| FrontierMath Tier 4 | — | 29.3% |
| MathArena Final-Answer Competitions | — | 67.6% |
| OTIS Mock AIME 2024-2025 | — | 86.4% |
| ProofBench | — | 35% |
| LMArena Math | — | 1482 |
Knowledge Not comparable
Devstral Small 2505: —, GLM-5.2: 57.1 (#40)
| Benchmark | Devstral Small 2505 | GLM-5.2 |
|---|---|---|
| GPQA Diamond | — | 91.9% |
| SimpleQA Verified | — | 34.2% |
| LMArena Expert | — | 1486 |
Multilingual Not comparable
Devstral Small 2505: —, GLM-5.2: 55.8 (#26)
| Benchmark | Devstral Small 2505 | GLM-5.2 |
|---|---|---|
| LMArena Non-English | — | 1459 |
| LMArena Chinese | — | 1519 |
| LMArena French | — | 1479 |
| LMArena German | — | 1468 |
| LMArena Japanese | — | 1451 |
| LMArena Korean | — | 1445 |
| LMArena Russian | — | 1466 |
| LMArena Spanish | — | 1477 |
Instruction Following Not comparable
Devstral Small 2505: —, GLM-5.2: 76.9 (#34)
| Benchmark | Devstral Small 2505 | GLM-5.2 |
|---|---|---|
| LMArena Instruction Following | — | 1465 |
Long Context Not comparable
Devstral Small 2505: —, GLM-5.2: 45.3 (#43)
| Benchmark | Devstral Small 2505 | GLM-5.2 |
|---|---|---|
| LMArena Longer Query | — | 1479 |
Writing & Preference Not comparable
Devstral Small 2505: —, GLM-5.2: 70.4 (#21)
| Benchmark | Devstral Small 2505 | GLM-5.2 |
|---|---|---|
| LMArena Text | — | 1470 |
| LMArena Creative Writing | — | 1462 |
| EQ-Bench Creative Writing | — | 1757 |
| EQ-Bench 4 | — | 1222 |
| LMArena Multi-Turn | — | 1469 |
Frequently asked questions
Is Devstral Small 2505 better than GLM-5.2?
GLM-5.2 is the stronger model overall, scoring 51.1 to 34.3 on the Noometry Index. Devstral Small 2505 costs 14× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.
Which is cheaper, Devstral Small 2505 or GLM-5.2?
Devstral Small 2505 is cheaper. It lists at $0.10 per million input tokens and $0.30 per million output tokens; GLM-5.2 lists at $1.40 and $4.40.
Is Devstral Small 2505 or GLM-5.2 better for coding?
GLM-5.2 scores higher on coding benchmarks: 51.3 versus 38.9 in the Noometry coding category.
Which has the bigger context window?
GLM-5.2 does, with 1M tokens against 128K.
How many benchmarks do Devstral Small 2505 and GLM-5.2 share?
3 benchmarks have published results for both models. Devstral Small 2505 has 4 scored results on Noometry and GLM-5.2 has 51.