Model comparison
GLM-5.1 vs Mistral Small
GLM-5.1 is the stronger model overall, scoring 47.8 to 33.4 on the Noometry Index. Mistral Small costs 8.2× less per token, which makes it the better buy when GLM-5.1's lead doesn't matter for your workload.
Last verified . 22 shared benchmarks.
Mistral AISummary
- They share 22 benchmarks with published results for both. GLM-5.1 scores higher in 8 categories and Mistral Small in 1 category; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.1 leads 49.7 to 16.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 93.3% for GLM-5.1 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-5.1.
- Mistral Small accepts more context: 262K tokens versus 200K.
Side by side
| GLM-5.1 | Mistral Small | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Mistral AI |
| Noometry Index | 47.8 | 33.4 |
| Released | 2026-04-07 | 2024-02-26 |
| Weights | Open | Open |
| Context window | 200K | 262K |
| Max output | 131K | 256K |
| Input $ / M tokens | $1.40 | $0.15 |
| Output $ / M tokens | $4.40 | $0.60 |
| Results tracked | 41 | 39 |
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Category by category
Coding GLM-5.1 leads
GLM-5.1: 48.7 (#55), Mistral Small: 34.0 (#247)
| Benchmark | GLM-5.1 | Mistral Small |
|---|---|---|
| SciCode | 43.8% | 26.5% |
| LMArena Coding | 1485 | 1362 |
| ALE-Bench | 887.1 | 497.62 |
| SWE-bench Verified | 74.2% | — |
| LMArena WebDev | 1508 | — |
| WeirdML | 57.1% | — |
| BigCodeBench Instruct | — | 36.1% |
| LiveBench Coding | — | 36.2% |
| BigCodeBench Complete | — | 46.6% |
Agentic & Tool Use Mistral Small leads
GLM-5.1: 24.9 (#113), Mistral Small: 28.1 (#93)
| Benchmark | GLM-5.1 | Mistral Small |
|---|---|---|
| APEX-Agents | 40.9% | — |
| Berkeley Function Calling Leaderboard | — | 37.1% |
| ExploitBench | 18.1% | — |
| GBAEval | 0% | — |
| Vending-Bench 2 | 5,634 | — |
Reasoning GLM-5.1 leads
GLM-5.1: 39.1 (#60), Mistral Small: 19.8 (#250)
| Benchmark | GLM-5.1 | Mistral Small |
|---|---|---|
| CritPt | 4.6% | 0% |
| LMArena Hard Prompts | 1472 | 1335 |
| SimpleBench | 55.1% | — |
| Kagi LLM Benchmark | — | 37.8% |
| NYT Connections (extended) | 77.7% | — |
| Chess Puzzles | 19% | — |
| Thematic Generalization | 69.8% | — |
| LiveBench Reasoning | — | 44.8% |
| DTBench | — | 70.9% |
| LiveBench Data Analysis | — | 53.7% |
| LMCA | — | 20.6% |
| Epoch Capabilities Index | 149.84 | — |
| LiveBench | — | 44% |
Math GLM-5.1 leads
GLM-5.1: 49.7 (#60), Mistral Small: 16.4 (#293)
| Benchmark | GLM-5.1 | Mistral Small |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 93.3% | 5.8% |
| LMArena Math | 1473 | 1341 |
| FrontierMath (Tiers 1-3) | 36.8% | — |
| MathArena Final-Answer Competitions | 67.1% | — |
| ProofBench | 22.2% | — |
| LiveBench Math | — | 39.9% |
| MATH Level 5 | — | 46.8% |
| FrontierMath (Feb 2025 set) | 33.4% | — |
| FrontierMath Tier 4 (v1) | 12.5% | — |
Knowledge GLM-5.1 leads
GLM-5.1: 54.9 (#50), Mistral Small: 31.0 (#222)
| Benchmark | GLM-5.1 | Mistral Small |
|---|---|---|
| GPQA Diamond | 89.9% | 47.5% |
| LMArena Expert | 1476 | 1291 |
| SimpleQA Verified | 34% | — |
| Vectara Hallucination Rate | — | 5.1% |
| MMLU | — | 68.7% |
Multimodal Not comparable
GLM-5.1: —, Mistral Small: 33.5 (#96)
| Benchmark | GLM-5.1 | Mistral Small |
|---|---|---|
| LMArena Vision | — | 1142 |
Multilingual GLM-5.1 leads
GLM-5.1: 55.0 (#36), Mistral Small: 45.5 (#169)
| Benchmark | GLM-5.1 | Mistral Small |
|---|---|---|
| LMArena Non-English | 1447 | 1315 |
| LMArena Chinese | 1515 | 1340 |
| LMArena French | 1474 | 1337 |
| LMArena German | 1465 | 1340 |
| LMArena Japanese | 1434 | 1275 |
| LMArena Korean | 1418 | 1259 |
| LMArena Russian | 1454 | 1324 |
| LMArena Spanish | 1469 | 1346 |
Instruction Following GLM-5.1 leads
GLM-5.1: 76.3 (#42), Mistral Small: 66.4 (#209)
| Benchmark | GLM-5.1 | Mistral Small |
|---|---|---|
| LMArena Instruction Following | 1451 | 1310 |
| LiveBench Instruction Following | — | 63.7% |
Long Context GLM-5.1 leads
GLM-5.1: 44.9 (#53), Mistral Small: 40.4 (#156)
| Benchmark | GLM-5.1 | Mistral Small |
|---|---|---|
| LMArena Longer Query | 1466 | 1327 |
Writing & Preference GLM-5.1 leads
GLM-5.1: 66.9 (#31), Mistral Small: 52.5 (#171)
| Benchmark | GLM-5.1 | Mistral Small |
|---|---|---|
| LMArena Text | 1461 | 1338 |
| LMArena Creative Writing | 1453 | 1305 |
| LMArena Multi-Turn | 1472 | 1344 |
| EQ-Bench Creative Writing | 1592 | — |
| LiveBench Language | — | 30.5% |
Frequently asked questions
Is GLM-5.1 better than Mistral Small?
GLM-5.1 is the stronger model overall, scoring 47.8 to 33.4 on the Noometry Index. Mistral Small costs 8.2× less per token, which makes it the better buy when GLM-5.1's lead doesn't matter for your workload.
Which is cheaper, GLM-5.1 or Mistral Small?
Mistral Small is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; GLM-5.1 lists at $1.40 and $4.40.
Is GLM-5.1 or Mistral Small better for coding?
GLM-5.1 scores higher on coding benchmarks: 48.7 versus 34.0 in the Noometry coding category.
Which has the bigger context window?
Mistral Small does, with 262K tokens against 200K.
How many benchmarks do GLM-5.1 and Mistral Small share?
22 benchmarks have published results for both models. GLM-5.1 has 41 scored results on Noometry and Mistral Small has 39.