Model comparison
GLM-5.2 vs Mistral Small
GLM-5.2 is the stronger model overall, scoring 51.1 to 33.4 on the Noometry Index. Mistral Small costs 8.2× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.
Last verified . 25 shared benchmarks.
Mistral AISummary
- They share 25 benchmarks with published results for both. GLM-5.2 scores higher in 9 categories and Mistral Small in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.2 leads 55.7 to 16.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 86.4% for GLM-5.2 and 5.8% for Mistral Small.
- Mistral Small is cheaper at $0.15 / $0.60 per million input/output tokens, against $1.40 / $4.40 for GLM-5.2.
- GLM-5.2 accepts more context: 1M tokens versus 262K.
Side by side
| GLM-5.2 | Mistral Small | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Mistral AI |
| Noometry Index | 51.1 | 33.4 |
| Released | 2026-06-13 | 2024-02-26 |
| Weights | Open | Open |
| Context window | 1M | 262K |
| Max output | 131K | 256K |
| Input $ / M tokens | $1.40 | $0.15 |
| Output $ / M tokens | $4.40 | $0.60 |
| Results tracked | 51 | 39 |
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Category by category
Coding GLM-5.2 leads
GLM-5.2: 51.3 (#41), Mistral Small: 34.0 (#247)
| Benchmark | GLM-5.2 | Mistral Small |
|---|---|---|
| SciCode | 50.5% | 26.5% |
| LMArena Coding | 1485 | 1362 |
| ALE-Bench | 1,047 | 497.62 |
| SWE-bench Verified | 78.7% | — |
| DeepSWE | 43.8% | — |
| FrontierCode | 24.5% | — |
| LMArena WebDev | 1603 | — |
| WeirdML | 70.1% | — |
| BigCodeBench Instruct | — | 36.1% |
| LiveBench Coding | — | 36.2% |
| BigCodeBench Complete | — | 46.6% |
Agentic & Tool Use GLM-5.2 leads
GLM-5.2: 32.4 (#63), Mistral Small: 28.1 (#93)
| Benchmark | GLM-5.2 | Mistral Small |
|---|---|---|
| APEX-Agents | 45.2% | — |
| Berkeley Function Calling Leaderboard | — | 37.1% |
| τ²-bench Banking | 37.1% | — |
| PostTrainBench | 31.7% | — |
| GBAEval | 0% | — |
| Vending-Bench 2 | 8,314 | — |
Reasoning GLM-5.2 leads
GLM-5.2: 42.3 (#52), Mistral Small: 19.8 (#250)
| Benchmark | GLM-5.2 | Mistral Small |
|---|---|---|
| Kagi LLM Benchmark | 62.6% | 37.8% |
| CritPt | 20.9% | 0% |
| LMArena Hard Prompts | 1480 | 1335 |
| DTBench | 93.6% | 70.9% |
| LMCA | 45.8% | 20.6% |
| ARC-AGI-2 | 22.8% | — |
| SimpleBench | 58.8% | — |
| NYT Connections (extended) | 74.3% | — |
| ARC-AGI-1 | 77% | — |
| Chess Puzzles | 21% | — |
| EBR-Bench | 9.5% | — |
| LiveBench Reasoning | — | 44.8% |
| Mystery Game Puzzles | 19% | — |
| LiveBench Data Analysis | — | 53.7% |
| Surface Evolver Bench | 55.6% | — |
| Epoch Capabilities Index | 151.78 | — |
| LiveBench | — | 44% |
Math GLM-5.2 leads
GLM-5.2: 55.7 (#43), Mistral Small: 16.4 (#293)
| Benchmark | GLM-5.2 | Mistral Small |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 86.4% | 5.8% |
| LMArena Math | 1482 | 1341 |
| FrontierMath (Tiers 1-3) | 59.2% | — |
| FrontierMath Tier 4 | 29.3% | — |
| MathArena Final-Answer Competitions | 67.6% | — |
| ProofBench | 35% | — |
| LiveBench Math | — | 39.9% |
| MATH Level 5 | — | 46.8% |
Knowledge GLM-5.2 leads
GLM-5.2: 57.1 (#40), Mistral Small: 31.0 (#222)
| Benchmark | GLM-5.2 | Mistral Small |
|---|---|---|
| GPQA Diamond | 91.9% | 47.5% |
| LMArena Expert | 1486 | 1291 |
| SimpleQA Verified | 34.2% | — |
| Vectara Hallucination Rate | — | 5.1% |
| MMLU | — | 68.7% |
Multimodal Not comparable
GLM-5.2: —, Mistral Small: 33.5 (#96)
| Benchmark | GLM-5.2 | Mistral Small |
|---|---|---|
| LMArena Vision | — | 1142 |
Multilingual GLM-5.2 leads
GLM-5.2: 55.8 (#26), Mistral Small: 45.5 (#169)
| Benchmark | GLM-5.2 | Mistral Small |
|---|---|---|
| LMArena Non-English | 1459 | 1315 |
| LMArena Chinese | 1519 | 1340 |
| LMArena French | 1479 | 1337 |
| LMArena German | 1468 | 1340 |
| LMArena Japanese | 1451 | 1275 |
| LMArena Korean | 1445 | 1259 |
| LMArena Russian | 1466 | 1324 |
| LMArena Spanish | 1477 | 1346 |
Instruction Following GLM-5.2 leads
GLM-5.2: 76.9 (#34), Mistral Small: 66.4 (#209)
| Benchmark | GLM-5.2 | Mistral Small |
|---|---|---|
| LMArena Instruction Following | 1465 | 1310 |
| LiveBench Instruction Following | — | 63.7% |
Long Context GLM-5.2 leads
GLM-5.2: 45.3 (#43), Mistral Small: 40.4 (#156)
| Benchmark | GLM-5.2 | Mistral Small |
|---|---|---|
| LMArena Longer Query | 1479 | 1327 |
Writing & Preference GLM-5.2 leads
GLM-5.2: 70.4 (#21), Mistral Small: 52.5 (#171)
| Benchmark | GLM-5.2 | Mistral Small |
|---|---|---|
| LMArena Text | 1470 | 1338 |
| LMArena Creative Writing | 1462 | 1305 |
| LMArena Multi-Turn | 1469 | 1344 |
| EQ-Bench Creative Writing | 1757 | — |
| EQ-Bench 4 | 1222 | — |
| LiveBench Language | — | 30.5% |
Frequently asked questions
Is GLM-5.2 better than Mistral Small?
GLM-5.2 is the stronger model overall, scoring 51.1 to 33.4 on the Noometry Index. Mistral Small costs 8.2× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.
Which is cheaper, GLM-5.2 or Mistral Small?
Mistral Small is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; GLM-5.2 lists at $1.40 and $4.40.
Is GLM-5.2 or Mistral Small better for coding?
GLM-5.2 scores higher on coding benchmarks: 51.3 versus 34.0 in the Noometry coding category.
Which has the bigger context window?
GLM-5.2 does, with 1M tokens against 262K.
How many benchmarks do GLM-5.2 and Mistral Small share?
25 benchmarks have published results for both models. GLM-5.2 has 51 scored results on Noometry and Mistral Small has 39.