Model comparison
GLM-5.1 vs Mixtral 8x7B
GLM-5.1 is the stronger model overall, scoring 47.6 to 26.8 on the Noometry Index. Mixtral 8x7B costs 3.1× less per token, which makes it the better buy when GLM-5.1's lead doesn't matter for your workload.
Last verified . 19 shared benchmarks.
Mistral AISummary
- They share 19 benchmarks with published results for both. GLM-5.1 scores higher in 8 categories and Mixtral 8x7B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-5.1 leads 55.0 to 11.0.
- The biggest single-benchmark swing is GPQA Diamond: 89.9% for GLM-5.1 and 30.6% for Mixtral 8x7B.
- Mixtral 8x7B is cheaper at $0.70 / $0.70 per million input/output tokens, against $1.40 / $4.40 for GLM-5.1.
- GLM-5.1 accepts more context: 200K tokens versus 32K.
Side by side
| GLM-5.1 | Mixtral 8x7B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Mistral AI |
| Noometry Index | 47.6 | 26.8 |
| Released | 2026-04-07 | 2023-12-11 |
| Weights | Open | Open |
| Context window | 200K | 32K |
| Max output | 131K | 32K |
| Input $ / M tokens | $1.40 | $0.70 |
| Output $ / M tokens | $4.40 | $0.70 |
| Results tracked | 41 | 38 |
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Category by category
Coding GLM-5.1 leads
GLM-5.1: 48.5 (#52), Mixtral 8x7B: 32.2 (#265)
| Benchmark | GLM-5.1 | Mixtral 8x7B |
|---|---|---|
| LMArena Coding | 1486 | 1127 |
| SWE-bench Verified | 74.2% | — |
| LMArena WebDev | 1509 | — |
| SciCode | 43.8% | — |
| WeirdML | 57.1% | — |
| ALE-Bench | 887.1 | — |
| HumanEval+ | — | 39.6% |
| MBPP+ | — | 49.7% |
Agentic & Tool Use Not comparable
GLM-5.1: 24.8 (#106), Mixtral 8x7B: —
| Benchmark | GLM-5.1 | Mixtral 8x7B |
|---|---|---|
| APEX-Agents | 40.9% | — |
| ExploitBench | 18.1% | — |
| GBAEval | 0% | — |
| Vending-Bench 2 | 5,634 | — |
Reasoning GLM-5.1 leads
GLM-5.1: 38.2 (#57), Mixtral 8x7B: 17.4 (#283)
| Benchmark | GLM-5.1 | Mixtral 8x7B |
|---|---|---|
| LMArena Hard Prompts | 1472 | 1115 |
| Epoch Capabilities Index | 149.86 | 118.38 |
| SimpleBench | 55.1% | — |
| NYT Connections (extended) | 77.7% | — |
| CritPt | 4.6% | — |
| Chess Puzzles | 19% | — |
| Thematic Generalization | 69.8% | — |
| DTBench | — | 49.6% |
| Adversarial NLI | — | 55.2% |
| ForecastBench | — | 56.3 |
| HellaSwag | — | 86.7% |
| PIQA | — | 83.6% |
| WinoGrande | — | 77.2% |
Math GLM-5.1 leads
GLM-5.1: 49.0 (#48), Mixtral 8x7B: 17.2 (#284)
| Benchmark | GLM-5.1 | Mixtral 8x7B |
|---|---|---|
| LMArena Math | 1472 | 1147 |
| FrontierMath (Tiers 1-3) | 36.8% | — |
| MathArena Final-Answer Competitions | 67.1% | — |
| OTIS Mock AIME 2024-2025 | 93.3% | — |
| ProofBench | 22.2% | — |
| Omni-MATH | — | 10.5% |
| MATH Level 5 | — | 10% |
| FrontierMath (Feb 2025 set) | 33.4% | — |
| FrontierMath Tier 4 (v1) | 12.5% | — |
| GSM8K | — | 74.4% |
Knowledge GLM-5.1 leads
GLM-5.1: 55.0 (#45), Mixtral 8x7B: 11.0 (#296)
| Benchmark | GLM-5.1 | Mixtral 8x7B |
|---|---|---|
| GPQA Diamond | 89.9% | 30.6% |
| LMArena Expert | 1477 | 1088 |
| SimpleQA Verified | 34% | — |
| MMLU-Pro | — | 33.5% |
| GPQA (HELM) | — | 29.6% |
| ARC (AI2) Challenge | — | 87.3% |
| MMLU | — | 70.6% |
| OpenBookQA | — | 85.8% |
| TriviaQA | — | 82.2% |
Multilingual GLM-5.1 leads
GLM-5.1: 55.2 (#34), Mixtral 8x7B: 29.8 (#261)
| Benchmark | GLM-5.1 | Mixtral 8x7B |
|---|---|---|
| LMArena Non-English | 1448 | 1077 |
| LMArena Chinese | 1516 | 1055 |
| LMArena French | 1478 | 1165 |
| LMArena German | 1466 | 1114 |
| LMArena Japanese | 1436 | 931 |
| LMArena Korean | 1423 | 968 |
| LMArena Russian | 1453 | 1090 |
| LMArena Spanish | 1467 | 1110 |
Instruction Following GLM-5.1 leads
GLM-5.1: 76.5 (#41), Mixtral 8x7B: 51.2 (#292)
| Benchmark | GLM-5.1 | Mixtral 8x7B |
|---|---|---|
| LMArena Instruction Following | 1451 | 1110 |
| IFEval | — | 57.5% |
Long Context GLM-5.1 leads
GLM-5.1: 44.9 (#51), Mixtral 8x7B: 33.4 (#255)
| Benchmark | GLM-5.1 | Mixtral 8x7B |
|---|---|---|
| LMArena Longer Query | 1467 | 1103 |
Writing & Preference GLM-5.1 leads
GLM-5.1: 67.2 (#29), Mixtral 8x7B: 34.4 (#265)
| Benchmark | GLM-5.1 | Mixtral 8x7B |
|---|---|---|
| LMArena Text | 1462 | 1132 |
| LMArena Creative Writing | 1454 | 1109 |
| LMArena Multi-Turn | 1473 | 1115 |
| EQ-Bench Creative Writing | 1592 | — |
| WildBench | — | 67.3% |
Frequently asked questions
Is GLM-5.1 better than Mixtral 8x7B?
GLM-5.1 is the stronger model overall, scoring 47.6 to 26.8 on the Noometry Index. Mixtral 8x7B costs 3.1× less per token, which makes it the better buy when GLM-5.1's lead doesn't matter for your workload.
Which is cheaper, GLM-5.1 or Mixtral 8x7B?
Mixtral 8x7B is cheaper. It lists at $0.70 per million input tokens and $0.70 per million output tokens; GLM-5.1 lists at $1.40 and $4.40.
Is GLM-5.1 or Mixtral 8x7B better for coding?
GLM-5.1 scores higher on coding benchmarks: 48.5 versus 32.2 in the Noometry coding category.
Which has the bigger context window?
GLM-5.1 does, with 200K tokens against 32K.
How many benchmarks do GLM-5.1 and Mixtral 8x7B share?
19 benchmarks have published results for both models. GLM-5.1 has 41 scored results on Noometry and Mixtral 8x7B has 38.