Model comparison
GLM-4.6V vs Mistral Small
GLM-4.6V is the stronger model overall, scoring 41.3 to 33.4 on the Noometry Index. Mistral Small costs 1.7× less per token, which makes it the better buy when GLM-4.6V's lead doesn't matter for your workload.
Last verified . 12 shared benchmarks.
Mistral AISummary
- They share 12 benchmarks with published results for both. GLM-4.6V scores higher in 8 categories and Mistral Small in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-4.6V leads 27.6 to 19.8.
- Mistral Small is cheaper at $0.15 / $0.60 per million input/output tokens, against $0.30 / $0.90 for GLM-4.6V.
- Mistral Small accepts more context: 262K tokens versus 128K.
Side by side
| GLM-4.6V | Mistral Small | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Mistral AI |
| Noometry Index | 41.3 | 33.4 |
| Released | 2025-12-08 | 2024-02-26 |
| Weights | Open | Open |
| Context window | 128K | 262K |
| Max output | 33K | 256K |
| Input $ / M tokens | $0.30 | $0.15 |
| Output $ / M tokens | $0.90 | $0.60 |
| Results tracked | 12 | 39 |
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Category by category
Coding GLM-4.6V leads
GLM-4.6V: 40.9 (#128), Mistral Small: 34.0 (#247)
| Benchmark | GLM-4.6V | Mistral Small |
|---|---|---|
| LMArena Coding | 1390 | 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-4.6V: —, Mistral Small: 28.1 (#93)
| Benchmark | GLM-4.6V | Mistral Small |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 37.1% |
Reasoning GLM-4.6V leads
GLM-4.6V: 27.6 (#115), Mistral Small: 19.8 (#250)
| Benchmark | GLM-4.6V | Mistral Small |
|---|---|---|
| LMArena Hard Prompts | 1368 | 1335 |
| Kagi LLM Benchmark | — | 37.8% |
| CritPt | — | 0% |
| LiveBench Reasoning | — | 44.8% |
| DTBench | — | 70.9% |
| LiveBench Data Analysis | — | 53.7% |
| LMCA | — | 20.6% |
| LiveBench | — | 44% |
Math Not comparable
GLM-4.6V: —, Mistral Small: 16.4 (#293)
| Benchmark | GLM-4.6V | Mistral Small |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 5.8% |
| LiveBench Math | — | 39.9% |
| LMArena Math | — | 1341 |
| MATH Level 5 | — | 46.8% |
Knowledge GLM-4.6V leads
GLM-4.6V: 38.0 (#149), Mistral Small: 31.0 (#222)
| Benchmark | GLM-4.6V | Mistral Small |
|---|---|---|
| LMArena Expert | 1371 | 1291 |
| GPQA Diamond | — | 47.5% |
| Vectara Hallucination Rate | — | 5.1% |
| MMLU | — | 68.7% |
Multimodal GLM-4.6V leads
GLM-4.6V: 34.8 (#90), Mistral Small: 33.5 (#96)
| Benchmark | GLM-4.6V | Mistral Small |
|---|---|---|
| LMArena Vision | 1164 | 1142 |
Multilingual GLM-4.6V leads
GLM-4.6V: 48.6 (#141), Mistral Small: 45.5 (#169)
| Benchmark | GLM-4.6V | Mistral Small |
|---|---|---|
| LMArena Non-English | 1359 | 1315 |
| LMArena Chinese | 1425 | 1340 |
| LMArena Russian | 1340 | 1324 |
| LMArena French | — | 1337 |
| LMArena German | — | 1340 |
| LMArena Japanese | — | 1275 |
| LMArena Korean | — | 1259 |
| LMArena Spanish | — | 1346 |
Instruction Following GLM-4.6V leads
GLM-4.6V: 71.4 (#151), Mistral Small: 66.4 (#209)
| Benchmark | GLM-4.6V | Mistral Small |
|---|---|---|
| LMArena Instruction Following | 1352 | 1310 |
| LiveBench Instruction Following | — | 63.7% |
Long Context Too close to call
GLM-4.6V: 41.3 (#143), Mistral Small: 40.4 (#156)
| Benchmark | GLM-4.6V | Mistral Small |
|---|---|---|
| LMArena Longer Query | 1358 | 1327 |
Writing & Preference GLM-4.6V leads
GLM-4.6V: 56.6 (#137), Mistral Small: 52.5 (#171)
| Benchmark | GLM-4.6V | Mistral Small |
|---|---|---|
| LMArena Text | 1377 | 1338 |
| LMArena Creative Writing | 1347 | 1305 |
| LMArena Multi-Turn | 1360 | 1344 |
| LiveBench Language | — | 30.5% |
Frequently asked questions
Is GLM-4.6V better than Mistral Small?
GLM-4.6V is the stronger model overall, scoring 41.3 to 33.4 on the Noometry Index. Mistral Small costs 1.7× less per token, which makes it the better buy when GLM-4.6V's lead doesn't matter for your workload.
Which is cheaper, GLM-4.6V or Mistral Small?
Mistral Small is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; GLM-4.6V lists at $0.30 and $0.90.
Is GLM-4.6V or Mistral Small better for coding?
GLM-4.6V scores higher on coding benchmarks: 40.9 versus 34.0 in the Noometry coding category.
Which has the bigger context window?
Mistral Small does, with 262K tokens against 128K.
How many benchmarks do GLM-4.6V and Mistral Small share?
12 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and Mistral Small has 39.