Model comparison
GLM-4.7 vs Mistral Small 3.1
GLM-4.7 is the stronger model overall, scoring 42.0 to 31.7 on the Noometry Index. Mistral Small 3.1 costs 2.5× less per token, which makes it the better buy when GLM-4.7'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-4.7 scores higher in 8 categories and Mistral Small 3.1 in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-4.7 leads 47.0 to 22.6.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 83.3% for GLM-4.7 and 3.9% for Mistral Small 3.1.
- Mistral Small 3.1 is cheaper at $0.35 / $0.56 per million input/output tokens, against $0.60 / $2.20 for GLM-4.7.
- GLM-4.7 accepts more context: 205K tokens versus 128K.
Side by side
| GLM-4.7 | Mistral Small 3.1 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Mistral AI |
| Noometry Index | 42.0 | 31.7 |
| Released | 2025-12-22 | 2025-03-17 |
| Weights | Open | Open |
| Context window | 205K | 128K |
| Max output | 131K | 102K |
| Input $ / M tokens | $0.60 | $0.35 |
| Output $ / M tokens | $2.20 | $0.56 |
| Results tracked | 36 | 28 |
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Category by category
Coding GLM-4.7 leads
GLM-4.7: 44.0 (#79), Mistral Small 3.1: 38.3 (#179)
| Benchmark | GLM-4.7 | Mistral Small 3.1 |
|---|---|---|
| LMArena Coding | 1454 | 1309 |
| LMArena WebDev | 1435 | — |
| SciCode | 45.1% | — |
| ALE-Bench | 399.48 | — |
Agentic & Tool Use Not comparable
GLM-4.7: 26.5 (#103), Mistral Small 3.1: —
| Benchmark | GLM-4.7 | Mistral Small 3.1 |
|---|---|---|
| Terminal-Bench | 33.4% | — |
| Vending-Bench 2 | 2,377 | — |
Reasoning GLM-4.7 leads
GLM-4.7: 24.3 (#164), Mistral Small 3.1: 19.7 (#254)
| Benchmark | GLM-4.7 | Mistral Small 3.1 |
|---|---|---|
| Chess Puzzles | 6% | 1% |
| LMArena Hard Prompts | 1443 | 1278 |
| Epoch Capabilities Index | 143.51 | 127.48 |
| SimpleBench | 47.7% | — |
| CritPt | 1.7% | — |
Math GLM-4.7 leads
GLM-4.7: 38.6 (#135), Mistral Small 3.1: 14.7 (#301)
| Benchmark | GLM-4.7 | Mistral Small 3.1 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 83.3% | 3.9% |
| LMArena Math | 1423 | 1262 |
| ProofBench | 6% | — |
| Omni-MATH | — | 24.8% |
| FrontierMath (Feb 2025 set) | 2.4% | — |
| FrontierMath Tier 4 (v1) | 0% | — |
Knowledge GLM-4.7 leads
GLM-4.7: 47.0 (#80), Mistral Small 3.1: 22.6 (#271)
| Benchmark | GLM-4.7 | Mistral Small 3.1 |
|---|---|---|
| GPQA Diamond | 83.3% | 41.9% |
| LMArena Expert | 1424 | 1257 |
| SimpleQA Verified | 32.2% | — |
| MMLU-Pro | — | 61% |
| Vectara Hallucination Rate | 11.7% | — |
| GPQA (HELM) | — | 39.2% |
Multimodal Not comparable
GLM-4.7: —, Mistral Small 3.1: 33.2 (#99)
| Benchmark | GLM-4.7 | Mistral Small 3.1 |
|---|---|---|
| LMArena Vision | — | 1136 |
Multilingual GLM-4.7 leads
GLM-4.7: 52.8 (#79), Mistral Small 3.1: 41.2 (#209)
| Benchmark | GLM-4.7 | Mistral Small 3.1 |
|---|---|---|
| LMArena Non-English | 1417 | 1255 |
| LMArena Chinese | 1495 | 1253 |
| LMArena French | 1432 | 1273 |
| LMArena German | 1424 | 1266 |
| LMArena Japanese | 1439 | 1208 |
| LMArena Korean | 1399 | 1206 |
| LMArena Russian | 1423 | 1263 |
| LMArena Spanish | 1434 | 1283 |
Instruction Following GLM-4.7 leads
GLM-4.7: 74.4 (#95), Mistral Small 3.1: 63.6 (#230)
| Benchmark | GLM-4.7 | Mistral Small 3.1 |
|---|---|---|
| LMArena Instruction Following | 1411 | 1264 |
| IFEval | — | 75% |
Long Context GLM-4.7 leads
GLM-4.7: 42.8 (#116), Mistral Small 3.1: 39.5 (#178)
| Benchmark | GLM-4.7 | Mistral Small 3.1 |
|---|---|---|
| LMArena Longer Query | 1432 | 1299 |
| CL-bench | 15.9% | — |
| CL-bench Life | 10.9% | — |
Writing & Preference GLM-4.7 leads
GLM-4.7: 60.9 (#93), Mistral Small 3.1: 37.0 (#259)
| Benchmark | GLM-4.7 | Mistral Small 3.1 |
|---|---|---|
| LMArena Text | 1435 | 1277 |
| LMArena Creative Writing | 1401 | 1253 |
| EQ-Bench Creative Writing | 1413 | 761 |
| LMArena Multi-Turn | 1446 | 1270 |
| WildBench | — | 78.8% |
Frequently asked questions
Is GLM-4.7 better than Mistral Small 3.1?
GLM-4.7 is the stronger model overall, scoring 42.0 to 31.7 on the Noometry Index. Mistral Small 3.1 costs 2.5× less per token, which makes it the better buy when GLM-4.7's lead doesn't matter for your workload.
Which is cheaper, GLM-4.7 or Mistral Small 3.1?
Mistral Small 3.1 is cheaper. It lists at $0.35 per million input tokens and $0.56 per million output tokens; GLM-4.7 lists at $0.60 and $2.20.
Is GLM-4.7 or Mistral Small 3.1 better for coding?
GLM-4.7 scores higher on coding benchmarks: 44.0 versus 38.3 in the Noometry coding category.
Which has the bigger context window?
GLM-4.7 does, with 205K tokens against 128K.
How many benchmarks do GLM-4.7 and Mistral Small 3.1 share?
22 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and Mistral Small 3.1 has 28.