Model comparison
GLM-4.7 vs Mistral 7B
GLM-4.7 is the stronger model overall, scoring 42.0 to 23.0 on the Noometry Index. Mistral 7B costs 4.0× less per token, which makes it the better buy when GLM-4.7's lead doesn't matter for your workload.
Last verified . 20 shared benchmarks.
Mistral AISummary
- They share 20 benchmarks with published results for both. GLM-4.7 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-4.7 leads 47.0 to 7.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 83.3% for GLM-4.7 and 0.3% for Mistral 7B.
- Mistral 7B is cheaper at $0.25 / $0.25 per million input/output tokens, against $0.60 / $2.20 for GLM-4.7.
- GLM-4.7 accepts more context: 205K tokens versus 8K.
Side by side
| GLM-4.7 | Mistral 7B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Mistral AI |
| Noometry Index | 42.0 | 23.0 |
| Released | 2025-12-22 | 2023-09-27 |
| Weights | Open | Open |
| Context window | 205K | 8K |
| Max output | 131K | 8K |
| Input $ / M tokens | $0.60 | $0.25 |
| Output $ / M tokens | $2.20 | $0.25 |
| Results tracked | 36 | 37 |
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Category by category
Coding GLM-4.7 leads
GLM-4.7: 44.0 (#79), Mistral 7B: 26.4 (#326)
| Benchmark | GLM-4.7 | Mistral 7B |
|---|---|---|
| LMArena Coding | 1454 | 1082 |
| LMArena WebDev | 1435 | — |
| SciCode | 45.1% | — |
| BigCodeBench Instruct | — | 19.5% |
| BigCodeBench Complete | — | 27.3% |
| ALE-Bench | 399.48 | — |
| HumanEval+ | — | 36% |
| MBPP+ | — | 42.1% |
Agentic & Tool Use Not comparable
GLM-4.7: 26.5 (#103), Mistral 7B: —
| Benchmark | GLM-4.7 | Mistral 7B |
|---|---|---|
| Terminal-Bench | 33.4% | — |
| Vending-Bench 2 | 2,377 | — |
Reasoning GLM-4.7 leads
GLM-4.7: 24.3 (#164), Mistral 7B: 13.1 (#336)
| Benchmark | GLM-4.7 | Mistral 7B |
|---|---|---|
| Chess Puzzles | 6% | 0% |
| LMArena Hard Prompts | 1443 | 1067 |
| Epoch Capabilities Index | 143.51 | 112.21 |
| SimpleBench | 47.7% | — |
| CritPt | 1.7% | — |
| DTBench | — | 42.5% |
| Adversarial NLI | — | 47.1% |
| BIG-Bench Hard | — | 56.1% |
| HellaSwag | — | 81% |
| PIQA | — | 83% |
| WinoGrande | — | 75.3% |
Math GLM-4.7 leads
GLM-4.7: 38.6 (#135), Mistral 7B: 8.1 (#325)
| Benchmark | GLM-4.7 | Mistral 7B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 83.3% | 0.3% |
| LMArena Math | 1423 | 1085 |
| ProofBench | 6% | — |
| MATH Level 5 | — | 3.7% |
| FrontierMath (Feb 2025 set) | 2.4% | — |
| FrontierMath Tier 4 (v1) | 0% | — |
| GSM8K | — | 54.4% |
Knowledge GLM-4.7 leads
GLM-4.7: 47.0 (#80), Mistral 7B: 7.4 (#311)
| Benchmark | GLM-4.7 | Mistral 7B |
|---|---|---|
| GPQA Diamond | 83.3% | 15.2% |
| LMArena Expert | 1424 | 1036 |
| SimpleQA Verified | 32.2% | — |
| Vectara Hallucination Rate | 11.7% | — |
| ARC (AI2) Challenge | — | 78.6% |
| BoolQ | — | 87.4% |
| MMLU | — | 62.5% |
| OpenBookQA | — | 79.8% |
| TriviaQA | — | 75.2% |
Multilingual GLM-4.7 leads
GLM-4.7: 52.8 (#79), Mistral 7B: 25.8 (#283)
| Benchmark | GLM-4.7 | Mistral 7B |
|---|---|---|
| LMArena Non-English | 1417 | 1012 |
| LMArena Chinese | 1495 | 1009 |
| LMArena French | 1432 | 1037 |
| LMArena German | 1424 | 987 |
| LMArena Japanese | 1439 | 878 |
| LMArena Russian | 1423 | 1018 |
| LMArena Spanish | 1434 | 1026 |
| LMArena Korean | 1399 | — |
Instruction Following GLM-4.7 leads
GLM-4.7: 74.4 (#95), Mistral 7B: 54.2 (#280)
| Benchmark | GLM-4.7 | Mistral 7B |
|---|---|---|
| LMArena Instruction Following | 1411 | 1060 |
Long Context GLM-4.7 leads
GLM-4.7: 42.8 (#116), Mistral 7B: 32.2 (#271)
| Benchmark | GLM-4.7 | Mistral 7B |
|---|---|---|
| LMArena Longer Query | 1432 | 1060 |
| CL-bench | 15.9% | — |
| CL-bench Life | 10.9% | — |
Writing & Preference GLM-4.7 leads
GLM-4.7: 60.9 (#93), Mistral 7B: 30.7 (#286)
| Benchmark | GLM-4.7 | Mistral 7B |
|---|---|---|
| LMArena Text | 1435 | 1090 |
| LMArena Creative Writing | 1401 | 1068 |
| LMArena Multi-Turn | 1446 | 1062 |
| EQ-Bench Creative Writing | 1413 | — |
Frequently asked questions
Is GLM-4.7 better than Mistral 7B?
GLM-4.7 is the stronger model overall, scoring 42.0 to 23.0 on the Noometry Index. Mistral 7B costs 4.0× 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 7B?
Mistral 7B is cheaper. It lists at $0.25 per million input tokens and $0.25 per million output tokens; GLM-4.7 lists at $0.60 and $2.20.
Is GLM-4.7 or Mistral 7B better for coding?
GLM-4.7 scores higher on coding benchmarks: 44.0 versus 26.4 in the Noometry coding category.
Which has the bigger context window?
GLM-4.7 does, with 205K tokens against 8K.
How many benchmarks do GLM-4.7 and Mistral 7B share?
20 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and Mistral 7B has 37.