Model comparison
GLM-5.2 vs Mistral 7B
GLM-5.2 is the stronger model overall, scoring 50.9 to 22.9 on the Noometry Index. Mistral 7B costs 8.6× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.
Last verified . 21 shared benchmarks.
Mistral AISummary
- They share 21 benchmarks with published results for both. GLM-5.2 scores higher in 8 categories and Mistral 7B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-5.2 leads 57.1 to 7.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 86.4% for GLM-5.2 and 0.3% for Mistral 7B.
- Mistral 7B is cheaper at $0.25 / $0.25 per million input/output tokens, against $1.40 / $4.40 for GLM-5.2.
- GLM-5.2 accepts more context: 1M tokens versus 8K.
Side by side
| GLM-5.2 | Mistral 7B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Mistral AI |
| Noometry Index | 50.9 | 22.9 |
| Released | 2026-06-13 | 2023-09-27 |
| Weights | Open | Open |
| Context window | 1M | 8K |
| Max output | 131K | 8K |
| Input $ / M tokens | $1.40 | $0.25 |
| Output $ / M tokens | $4.40 | $0.25 |
| Results tracked | 50 | 37 |
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Category by category
Coding GLM-5.2 leads
GLM-5.2: 50.9 (#37), Mistral 7B: 25.9 (#321)
| Benchmark | GLM-5.2 | Mistral 7B |
|---|---|---|
| LMArena Coding | 1484 | 1082 |
| SWE-bench Verified | 78.7% | — |
| DeepSWE | 43.8% | — |
| FrontierCode | 24.5% | — |
| LMArena WebDev | 1604 | — |
| SciCode | 50.5% | — |
| WeirdML | 70.1% | — |
| BigCodeBench Instruct | — | 19.5% |
| BigCodeBench Complete | — | 27.3% |
| ALE-Bench | 1,047 | — |
| HumanEval+ | — | 36% |
| MBPP+ | — | 42.1% |
Agentic & Tool Use Not comparable
GLM-5.2: 31.6 (#63), Mistral 7B: —
| Benchmark | GLM-5.2 | Mistral 7B |
|---|---|---|
| τ²-bench Banking | 37.1% | — |
| PostTrainBench | 31.7% | — |
| GBAEval | 0% | — |
| Vending-Bench 2 | 8,314 | — |
Reasoning GLM-5.2 leads
GLM-5.2: 42.1 (#46), Mistral 7B: 12.6 (#331)
| Benchmark | GLM-5.2 | Mistral 7B |
|---|---|---|
| Chess Puzzles | 21% | 0% |
| LMArena Hard Prompts | 1480 | 1067 |
| DTBench | 93.6% | 42.5% |
| Epoch Capabilities Index | 151.76 | 111.97 |
| ARC-AGI-2 | 22.8% | — |
| SimpleBench | 58.8% | — |
| Kagi LLM Benchmark | 62.6% | — |
| NYT Connections (extended) | 74.3% | — |
| ARC-AGI-1 | 77% | — |
| CritPt | 20.9% | — |
| EBR-Bench | 9.5% | — |
| Mystery Game Puzzles | 19% | — |
| LMCA | 45.8% | — |
| Surface Evolver Bench | 55.6% | — |
| Adversarial NLI | — | 47.1% |
| BIG-Bench Hard | — | 56.1% |
| HellaSwag | — | 81% |
| PIQA | — | 83% |
| WinoGrande | — | 75.3% |
Math GLM-5.2 leads
GLM-5.2: 55.4 (#35), Mistral 7B: 7.6 (#319)
| Benchmark | GLM-5.2 | Mistral 7B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 86.4% | 0.3% |
| LMArena Math | 1482 | 1085 |
| FrontierMath (Tiers 1-3) | 59.2% | — |
| FrontierMath Tier 4 | 29.3% | — |
| MathArena Final-Answer Competitions | 67.6% | — |
| ProofBench | 35% | — |
| MATH Level 5 | — | 3.7% |
| GSM8K | — | 54.4% |
Knowledge GLM-5.2 leads
GLM-5.2: 57.1 (#36), Mistral 7B: 7.3 (#306)
| Benchmark | GLM-5.2 | Mistral 7B |
|---|---|---|
| GPQA Diamond | 91.9% | 15.2% |
| LMArena Expert | 1484 | 1036 |
| SimpleQA Verified | 34.2% | — |
| ARC (AI2) Challenge | — | 78.6% |
| BoolQ | — | 87.4% |
| MMLU | — | 62.5% |
| OpenBookQA | — | 79.8% |
| TriviaQA | — | 75.2% |
Multilingual GLM-5.2 leads
GLM-5.2: 56.0 (#25), Mistral 7B: 26.0 (#278)
| Benchmark | GLM-5.2 | Mistral 7B |
|---|---|---|
| LMArena Non-English | 1459 | 1012 |
| LMArena Chinese | 1518 | 1009 |
| LMArena French | 1476 | 1036 |
| LMArena German | 1470 | 987 |
| LMArena Japanese | 1451 | 878 |
| LMArena Russian | 1467 | 1018 |
| LMArena Spanish | 1477 | 1026 |
| LMArena Korean | 1445 | — |
Instruction Following GLM-5.2 leads
GLM-5.2: 77.1 (#32), Mistral 7B: 54.5 (#275)
| Benchmark | GLM-5.2 | Mistral 7B |
|---|---|---|
| LMArena Instruction Following | 1465 | 1060 |
Long Context GLM-5.2 leads
GLM-5.2: 45.3 (#43), Mistral 7B: 32.2 (#266)
| Benchmark | GLM-5.2 | Mistral 7B |
|---|---|---|
| LMArena Longer Query | 1479 | 1060 |
Writing & Preference GLM-5.2 leads
GLM-5.2: 70.7 (#20), Mistral 7B: 30.9 (#281)
| Benchmark | GLM-5.2 | Mistral 7B |
|---|---|---|
| LMArena Text | 1470 | 1090 |
| LMArena Creative Writing | 1465 | 1068 |
| LMArena Multi-Turn | 1468 | 1062 |
| EQ-Bench Creative Writing | 1757 | — |
| EQ-Bench 4 | 1222 | — |
Frequently asked questions
Is GLM-5.2 better than Mistral 7B?
GLM-5.2 is the stronger model overall, scoring 50.9 to 22.9 on the Noometry Index. Mistral 7B costs 8.6× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.
Which is cheaper, GLM-5.2 or Mistral 7B?
Mistral 7B is cheaper. It lists at $0.25 per million input tokens and $0.25 per million output tokens; GLM-5.2 lists at $1.40 and $4.40.
Is GLM-5.2 or Mistral 7B better for coding?
GLM-5.2 scores higher on coding benchmarks: 50.9 versus 25.9 in the Noometry coding category.
Which has the bigger context window?
GLM-5.2 does, with 1M tokens against 8K.
How many benchmarks do GLM-5.2 and Mistral 7B share?
21 benchmarks have published results for both models. GLM-5.2 has 50 scored results on Noometry and Mistral 7B has 37.