Model comparison
GLM-4.7 vs Mistral Small
GLM-4.7 is the stronger model overall, scoring 42.0 to 33.4 on the Noometry Index. Mistral Small costs 3.8× less per token, which makes it the better buy when GLM-4.7's lead doesn't matter for your workload.
Last verified . 23 shared benchmarks.
Mistral AISummary
- They share 23 benchmarks with published results for both. GLM-4.7 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-4.7 leads 38.6 to 16.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 83.3% for GLM-4.7 and 5.8% for Mistral Small.
- Mistral Small is cheaper at $0.15 / $0.60 per million input/output tokens, against $0.60 / $2.20 for GLM-4.7.
- Mistral Small accepts more context: 262K tokens versus 205K.
Side by side
| GLM-4.7 | Mistral Small | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Mistral AI |
| Noometry Index | 42.0 | 33.4 |
| Released | 2025-12-22 | 2024-02-26 |
| Weights | Open | Open |
| Context window | 205K | 262K |
| Max output | 131K | 256K |
| Input $ / M tokens | $0.60 | $0.15 |
| Output $ / M tokens | $2.20 | $0.60 |
| Results tracked | 36 | 39 |
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Category by category
Coding GLM-4.7 leads
GLM-4.7: 44.0 (#79), Mistral Small: 34.0 (#247)
| Benchmark | GLM-4.7 | Mistral Small |
|---|---|---|
| SciCode | 45.1% | 26.5% |
| LMArena Coding | 1454 | 1362 |
| ALE-Bench | 399.48 | 497.62 |
| LMArena WebDev | 1435 | — |
| BigCodeBench Instruct | — | 36.1% |
| LiveBench Coding | — | 36.2% |
| BigCodeBench Complete | — | 46.6% |
Agentic & Tool Use Mistral Small leads
GLM-4.7: 26.5 (#103), Mistral Small: 28.1 (#93)
| Benchmark | GLM-4.7 | Mistral Small |
|---|---|---|
| Terminal-Bench | 33.4% | — |
| Berkeley Function Calling Leaderboard | — | 37.1% |
| Vending-Bench 2 | 2,377 | — |
Reasoning GLM-4.7 leads
GLM-4.7: 24.3 (#164), Mistral Small: 19.8 (#250)
| Benchmark | GLM-4.7 | Mistral Small |
|---|---|---|
| CritPt | 1.7% | 0% |
| LMArena Hard Prompts | 1443 | 1335 |
| SimpleBench | 47.7% | — |
| Kagi LLM Benchmark | — | 37.8% |
| Chess Puzzles | 6% | — |
| LiveBench Reasoning | — | 44.8% |
| DTBench | — | 70.9% |
| LiveBench Data Analysis | — | 53.7% |
| LMCA | — | 20.6% |
| Epoch Capabilities Index | 143.51 | — |
| LiveBench | — | 44% |
Math GLM-4.7 leads
GLM-4.7: 38.6 (#135), Mistral Small: 16.4 (#293)
| Benchmark | GLM-4.7 | Mistral Small |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 83.3% | 5.8% |
| LMArena Math | 1423 | 1341 |
| ProofBench | 6% | — |
| LiveBench Math | — | 39.9% |
| MATH Level 5 | — | 46.8% |
| FrontierMath (Feb 2025 set) | 2.4% | — |
| FrontierMath Tier 4 (v1) | 0% | — |
Knowledge GLM-4.7 leads
GLM-4.7: 47.0 (#80), Mistral Small: 31.0 (#222)
| Benchmark | GLM-4.7 | Mistral Small |
|---|---|---|
| GPQA Diamond | 83.3% | 47.5% |
| Vectara Hallucination Rate | 11.7% | 5.1% |
| LMArena Expert | 1424 | 1291 |
| SimpleQA Verified | 32.2% | — |
| MMLU | — | 68.7% |
Multimodal Not comparable
GLM-4.7: —, Mistral Small: 33.5 (#96)
| Benchmark | GLM-4.7 | Mistral Small |
|---|---|---|
| LMArena Vision | — | 1142 |
Multilingual GLM-4.7 leads
GLM-4.7: 52.8 (#79), Mistral Small: 45.5 (#169)
| Benchmark | GLM-4.7 | Mistral Small |
|---|---|---|
| LMArena Non-English | 1417 | 1315 |
| LMArena Chinese | 1495 | 1340 |
| LMArena French | 1432 | 1337 |
| LMArena German | 1424 | 1340 |
| LMArena Japanese | 1439 | 1275 |
| LMArena Korean | 1399 | 1259 |
| LMArena Russian | 1423 | 1324 |
| LMArena Spanish | 1434 | 1346 |
Instruction Following GLM-4.7 leads
GLM-4.7: 74.4 (#95), Mistral Small: 66.4 (#209)
| Benchmark | GLM-4.7 | Mistral Small |
|---|---|---|
| LMArena Instruction Following | 1411 | 1310 |
| LiveBench Instruction Following | — | 63.7% |
Long Context GLM-4.7 leads
GLM-4.7: 42.8 (#116), Mistral Small: 40.4 (#156)
| Benchmark | GLM-4.7 | Mistral Small |
|---|---|---|
| LMArena Longer Query | 1432 | 1327 |
| CL-bench | 15.9% | — |
| CL-bench Life | 10.9% | — |
Writing & Preference GLM-4.7 leads
GLM-4.7: 60.9 (#93), Mistral Small: 52.5 (#171)
| Benchmark | GLM-4.7 | Mistral Small |
|---|---|---|
| LMArena Text | 1435 | 1338 |
| LMArena Creative Writing | 1401 | 1305 |
| LMArena Multi-Turn | 1446 | 1344 |
| EQ-Bench Creative Writing | 1413 | — |
| LiveBench Language | — | 30.5% |
Frequently asked questions
Is GLM-4.7 better than Mistral Small?
GLM-4.7 is the stronger model overall, scoring 42.0 to 33.4 on the Noometry Index. Mistral Small costs 3.8× 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?
Mistral Small is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; GLM-4.7 lists at $0.60 and $2.20.
Is GLM-4.7 or Mistral Small better for coding?
GLM-4.7 scores higher on coding benchmarks: 44.0 versus 34.0 in the Noometry coding category.
Which has the bigger context window?
Mistral Small does, with 262K tokens against 205K.
How many benchmarks do GLM-4.7 and Mistral Small share?
23 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and Mistral Small has 39.