Model comparison
GLM-4.7-Flash vs Mistral Small
GLM-4.7-Flash is the stronger model overall, scoring 38.8 to 33.4 on the Noometry Index.
Last verified . 19 shared benchmarks.
Summary
- They share 19 benchmarks with published results for both. GLM-4.7-Flash scores higher in 7 categories and Mistral Small in 1 category; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-4.7-Flash leads 36.1 to 16.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 58.3% for GLM-4.7-Flash and 5.8% for Mistral Small.
- GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $0.15 / $0.60 for Mistral Small.
- Mistral Small accepts more context: 262K tokens versus 200K.
Side by side
| GLM-4.7-Flash | Mistral Small | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Mistral AI |
| Noometry Index | 38.8 | 33.4 |
| Released | 2026-01-19 | 2024-02-26 |
| Weights | Open | Open |
| Context window | 200K | 262K |
| Max output | 131K | 256K |
| Input $ / M tokens | $0.06 | $0.15 |
| Output $ / M tokens | $0.40 | $0.60 |
| Results tracked | 21 | 39 |
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Category by category
Coding GLM-4.7-Flash leads
GLM-4.7-Flash: 40.6 (#135), Mistral Small: 34.0 (#247)
| Benchmark | GLM-4.7-Flash | Mistral Small |
|---|---|---|
| LMArena Coding | 1383 | 1362 |
| SciCode | — | 26.5% |
| BigCodeBench Instruct | — | 36.1% |
| LiveBench Coding | — | 36.2% |
| BigCodeBench Complete | — | 46.6% |
| ALE-Bench | — | 497.62 |
Agentic & Tool Use Not comparable
GLM-4.7-Flash: —, Mistral Small: 28.1 (#93)
| Benchmark | GLM-4.7-Flash | Mistral Small |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 37.1% |
Reasoning GLM-4.7-Flash leads
GLM-4.7-Flash: 20.9 (#229), Mistral Small: 19.8 (#250)
| Benchmark | GLM-4.7-Flash | Mistral Small |
|---|---|---|
| LMArena Hard Prompts | 1356 | 1335 |
| Kagi LLM Benchmark | — | 37.8% |
| CritPt | — | 0% |
| Chess Puzzles | 0% | — |
| LiveBench Reasoning | — | 44.8% |
| DTBench | — | 70.9% |
| LiveBench Data Analysis | — | 53.7% |
| LMCA | — | 20.6% |
| LiveBench | — | 44% |
Math GLM-4.7-Flash leads
GLM-4.7-Flash: 36.1 (#173), Mistral Small: 16.4 (#293)
| Benchmark | GLM-4.7-Flash | Mistral Small |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 58.3% | 5.8% |
| LMArena Math | 1355 | 1341 |
| LiveBench Math | — | 39.9% |
| MATH Level 5 | — | 46.8% |
Knowledge GLM-4.7-Flash leads
GLM-4.7-Flash: 35.5 (#184), Mistral Small: 31.0 (#222)
| Benchmark | GLM-4.7-Flash | Mistral Small |
|---|---|---|
| GPQA Diamond | 60.5% | 47.5% |
| Vectara Hallucination Rate | 9.3% | 5.1% |
| LMArena Expert | 1357 | 1291 |
| MMLU | — | 68.7% |
Multimodal Not comparable
GLM-4.7-Flash: —, Mistral Small: 33.5 (#96)
| Benchmark | GLM-4.7-Flash | Mistral Small |
|---|---|---|
| LMArena Vision | — | 1142 |
Multilingual GLM-4.7-Flash leads
GLM-4.7-Flash: 46.5 (#158), Mistral Small: 45.5 (#169)
| Benchmark | GLM-4.7-Flash | Mistral Small |
|---|---|---|
| LMArena Non-English | 1330 | 1315 |
| LMArena Chinese | 1403 | 1340 |
| LMArena French | 1332 | 1337 |
| LMArena German | 1337 | 1340 |
| LMArena Korean | 1283 | 1259 |
| LMArena Russian | 1332 | 1324 |
| LMArena Spanish | 1350 | 1346 |
| LMArena Japanese | — | 1275 |
Instruction Following GLM-4.7-Flash leads
GLM-4.7-Flash: 70.1 (#167), Mistral Small: 66.4 (#209)
| Benchmark | GLM-4.7-Flash | Mistral Small |
|---|---|---|
| LMArena Instruction Following | 1327 | 1310 |
| LiveBench Instruction Following | — | 63.7% |
Long Context Too close to call
GLM-4.7-Flash: 40.9 (#148), Mistral Small: 40.4 (#156)
| Benchmark | GLM-4.7-Flash | Mistral Small |
|---|---|---|
| LMArena Longer Query | 1345 | 1327 |
Writing & Preference Mistral Small leads
GLM-4.7-Flash: 47.4 (#210), Mistral Small: 52.5 (#171)
| Benchmark | GLM-4.7-Flash | Mistral Small |
|---|---|---|
| LMArena Text | 1351 | 1338 |
| LMArena Creative Writing | 1297 | 1305 |
| LMArena Multi-Turn | 1342 | 1344 |
| EQ-Bench Creative Writing | 1125 | — |
| LiveBench Language | — | 30.5% |
Frequently asked questions
Is GLM-4.7-Flash better than Mistral Small?
GLM-4.7-Flash is the stronger model overall, scoring 38.8 to 33.4 on the Noometry Index.
Which is cheaper, GLM-4.7-Flash or Mistral Small?
GLM-4.7-Flash is cheaper. It lists at $0.06 per million input tokens and $0.40 per million output tokens; Mistral Small lists at $0.15 and $0.60.
Is GLM-4.7-Flash or Mistral Small better for coding?
GLM-4.7-Flash scores higher on coding benchmarks: 40.6 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-4.7-Flash and Mistral Small share?
19 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and Mistral Small has 39.