Model comparison
GLM-5 vs Mistral Small
GLM-5 is the stronger model overall, scoring 46.1 to 33.4 on the Noometry Index. Mistral Small costs 5.9× less per token, which makes it the better buy when GLM-5'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 scores higher in 9 categories and Mistral Small in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5 leads 46.4 to 16.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 80% for GLM-5 and 5.8% for Mistral Small.
- Mistral Small is cheaper at $0.15 / $0.60 per million input/output tokens, against $1 / $3.20 for GLM-5.
- Mistral Small accepts more context: 262K tokens versus 205K.
Side by side
| GLM-5 | Mistral Small | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Mistral AI |
| Noometry Index | 46.1 | 33.4 |
| Released | 2026-02-11 | 2024-02-26 |
| Weights | Open | Open |
| Context window | 205K | 262K |
| Max output | 131K | 256K |
| Input $ / M tokens | $1 | $0.15 |
| Output $ / M tokens | $3.20 | $0.60 |
| Results tracked | 45 | 39 |
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Category by category
Coding GLM-5 leads
GLM-5: 49.0 (#52), Mistral Small: 34.0 (#247)
| Benchmark | GLM-5 | Mistral Small |
|---|---|---|
| LMArena Coding | 1461 | 1362 |
| ALE-Bench | 765.62 | 497.62 |
| SWE-bench Verified | 72.1% | — |
| SWE-bench Verified (bash only) | 72.8% | — |
| LMArena WebDev | 1434 | — |
| SWE-bench Multilingual | 69.7% | — |
| SciCode | — | 26.5% |
| WeirdML | 48.2% | — |
| BigCodeBench Instruct | — | 36.1% |
| LiveBench Coding | — | 36.2% |
| BigCodeBench Complete | — | 46.6% |
Agentic & Tool Use GLM-5 leads
GLM-5: 31.1 (#71), Mistral Small: 28.1 (#93)
| Benchmark | GLM-5 | Mistral Small |
|---|---|---|
| Terminal-Bench | 52.4% | — |
| Berkeley Function Calling Leaderboard | — | 37.1% |
| τ²-bench Airline | 82.5% | — |
| τ²-bench Banking | 9.8% | — |
| τ²-bench Retail | 73.7% | — |
| τ²-bench Telecom | 86.8% | — |
| Vending-Bench 2 | 4,432 | — |
Reasoning GLM-5 leads
GLM-5: 27.6 (#116), Mistral Small: 19.8 (#250)
| Benchmark | GLM-5 | Mistral Small |
|---|---|---|
| Kagi LLM Benchmark | 75% | 37.8% |
| LMArena Hard Prompts | 1452 | 1335 |
| ARC-AGI-2 | 4.9% | — |
| SimpleBench | 53.2% | — |
| NYT Connections (extended) | 74.8% | — |
| ARC-AGI-1 | 44.7% | — |
| CritPt | — | 0% |
| Chess Puzzles | 10% | — |
| LiveBench Reasoning | — | 44.8% |
| DTBench | — | 70.9% |
| LiveBench Data Analysis | — | 53.7% |
| LMCA | — | 20.6% |
| Epoch Capabilities Index | 145.83 | — |
| ForecastBench | 61 | — |
| LiveBench | — | 44% |
Math GLM-5 leads
GLM-5: 46.4 (#71), Mistral Small: 16.4 (#293)
| Benchmark | GLM-5 | Mistral Small |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 80% | 5.8% |
| LMArena Math | 1440 | 1341 |
| MathArena Final-Answer Competitions | 65.7% | — |
| LiveBench Math | — | 39.9% |
| MATH Level 5 | — | 46.8% |
| FrontierMath (Feb 2025 set) | 16.4% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge GLM-5 leads
GLM-5: 52.3 (#64), Mistral Small: 31.0 (#222)
| Benchmark | GLM-5 | Mistral Small |
|---|---|---|
| GPQA Diamond | 87.8% | 47.5% |
| Vectara Hallucination Rate | 10.1% | 5.1% |
| LMArena Expert | 1454 | 1291 |
| MMLU | — | 68.7% |
Multimodal Not comparable
GLM-5: —, Mistral Small: 33.5 (#96)
| Benchmark | GLM-5 | Mistral Small |
|---|---|---|
| LMArena Vision | — | 1142 |
Multilingual GLM-5 leads
GLM-5: 53.7 (#58), Mistral Small: 45.5 (#169)
| Benchmark | GLM-5 | Mistral Small |
|---|---|---|
| LMArena Non-English | 1430 | 1315 |
| LMArena Chinese | 1511 | 1340 |
| LMArena French | 1455 | 1337 |
| LMArena German | 1445 | 1340 |
| LMArena Japanese | 1416 | 1275 |
| LMArena Korean | 1423 | 1259 |
| LMArena Russian | 1436 | 1324 |
| LMArena Spanish | 1454 | 1346 |
Instruction Following GLM-5 leads
GLM-5: 75.2 (#67), Mistral Small: 66.4 (#209)
| Benchmark | GLM-5 | Mistral Small |
|---|---|---|
| LMArena Instruction Following | 1428 | 1310 |
| LiveBench Instruction Following | — | 63.7% |
Long Context GLM-5 leads
GLM-5: 44.7 (#60), Mistral Small: 40.4 (#156)
| Benchmark | GLM-5 | Mistral Small |
|---|---|---|
| LMArena Longer Query | 1446 | 1327 |
| CL-bench | 18.7% | — |
Writing & Preference GLM-5 leads
GLM-5: 66.0 (#38), Mistral Small: 52.5 (#171)
| Benchmark | GLM-5 | Mistral Small |
|---|---|---|
| LMArena Text | 1446 | 1338 |
| LMArena Creative Writing | 1439 | 1305 |
| LMArena Multi-Turn | 1456 | 1344 |
| EQ-Bench Creative Writing | 1601 | — |
| LiveBench Language | — | 30.5% |
Frequently asked questions
Is GLM-5 better than Mistral Small?
GLM-5 is the stronger model overall, scoring 46.1 to 33.4 on the Noometry Index. Mistral Small costs 5.9× less per token, which makes it the better buy when GLM-5's lead doesn't matter for your workload.
Which is cheaper, GLM-5 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 lists at $1 and $3.20.
Is GLM-5 or Mistral Small better for coding?
GLM-5 scores higher on coding benchmarks: 49.0 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-5 and Mistral Small share?
22 benchmarks have published results for both models. GLM-5 has 45 scored results on Noometry and Mistral Small has 39.