Model comparison
GLM-5.3 vs Mixtral 8x7B
GLM-5.3 is the stronger model overall, scoring 54.8 to 27.1 on the Noometry Index. Mixtral 8x7B costs 3.1× less per token, which makes it the better buy when GLM-5.3'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-5.3 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-5.3 leads 58.3 to 11.0.
- The biggest single-benchmark swing is GPQA Diamond: 90.9% for GLM-5.3 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-5.3.
- GLM-5.3 accepts more context: 1M tokens versus 32K.
Side by side
| GLM-5.3 | Mixtral 8x7B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Mistral AI |
| Noometry Index | 54.8 | 27.1 |
| Released | 2026-08-14 | 2023-12-11 |
| Weights | Open | Open |
| Context window | 1M | 32K |
| Max output | 131K | 32K |
| Input $ / M tokens | $1.40 | $0.70 |
| Output $ / M tokens | $4.40 | $0.70 |
| Results tracked | 42 | 38 |
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Category by category
Coding GLM-5.3 leads
GLM-5.3: 59.5 (#14), Mixtral 8x7B: 32.8 (#269)
| Benchmark | GLM-5.3 | Mixtral 8x7B |
|---|---|---|
| LMArena Coding | 1496 | 1126 |
| DeepSWE | 69% | — |
| FrontierCode | 40.1% | — |
| CursorBench | 42.6% | — |
| LMArena WebDev | 1622 | — |
| FrontierSWE | 30.2% | — |
| SciCode | 59% | — |
| WeirdML | 75.4% | — |
| ALE-Bench | 1,317 | — |
| HumanEval+ | — | 39.6% |
| MBPP+ | — | 49.7% |
Agentic & Tool Use Not comparable
GLM-5.3: 36.4 (#38), Mixtral 8x7B: —
| Benchmark | GLM-5.3 | Mixtral 8x7B |
|---|---|---|
| APEX-Agents | 56.6% | — |
| Vending-Bench 2 | 8,164 | — |
Reasoning GLM-5.3 leads
GLM-5.3: 46.1 (#46), Mixtral 8x7B: 18.2 (#285)
| Benchmark | GLM-5.3 | Mixtral 8x7B |
|---|---|---|
| LMArena Hard Prompts | 1489 | 1115 |
| DTBench | 87.7% | 49.6% |
| Epoch Capabilities Index | 155.61 | 118.47 |
| NYT Connections (extended) | 74.2% | — |
| CritPt | 19.1% | — |
| Chess Puzzles | 21% | — |
| Mystery Game Puzzles | 33% | — |
| LMCA | 55.5% | — |
| Adversarial NLI | — | 55.2% |
| Bench to the Future 3 | 0.15 | — |
| ForecastBench | — | 56.3 |
| HellaSwag | — | 86.7% |
| PIQA | — | 83.6% |
| WinoGrande | — | 77.2% |
Math GLM-5.3 leads
GLM-5.3: 62.3 (#33), Mixtral 8x7B: 18.8 (#289)
| Benchmark | GLM-5.3 | Mixtral 8x7B |
|---|---|---|
| LMArena Math | 1489 | 1147 |
| FrontierMath (Tiers 1-3) | 68.8% | — |
| FrontierMath Tier 4 | 29.3% | — |
| OTIS Mock AIME 2024-2025 | 91.1% | — |
| ProofBench | 49% | — |
| Omni-MATH | — | 10.5% |
| MATH Level 5 | — | 10% |
| GSM8K | — | 74.4% |
Knowledge GLM-5.3 leads
GLM-5.3: 58.3 (#37), Mixtral 8x7B: 11.0 (#301)
| Benchmark | GLM-5.3 | Mixtral 8x7B |
|---|---|---|
| GPQA Diamond | 90.9% | 30.6% |
| LMArena Expert | 1516 | 1088 |
| SimpleQA Verified | 41% | — |
| MMLU-Pro | — | 33.5% |
| GPQA (HELM) | — | 29.6% |
| ARC (AI2) Challenge | — | 87.3% |
| MMLU | — | 70.6% |
| OpenBookQA | — | 85.8% |
| TriviaQA | — | 82.2% |
Multilingual GLM-5.3 leads
GLM-5.3: 55.7 (#28), Mixtral 8x7B: 29.6 (#266)
| Benchmark | GLM-5.3 | Mixtral 8x7B |
|---|---|---|
| LMArena Non-English | 1457 | 1077 |
| LMArena Chinese | 1528 | 1055 |
| LMArena French | 1499 | 1166 |
| LMArena German | 1499 | 1114 |
| LMArena Japanese | 1453 | 931 |
| LMArena Korean | 1472 | 968 |
| LMArena Russian | 1463 | 1090 |
| LMArena Spanish | 1460 | 1111 |
Instruction Following GLM-5.3 leads
GLM-5.3: 77.5 (#23), Mixtral 8x7B: 51.0 (#297)
| Benchmark | GLM-5.3 | Mixtral 8x7B |
|---|---|---|
| LMArena Instruction Following | 1477 | 1109 |
| IFEval | — | 57.5% |
Long Context GLM-5.3 leads
GLM-5.3: 45.4 (#41), Mixtral 8x7B: 33.4 (#260)
| Benchmark | GLM-5.3 | Mixtral 8x7B |
|---|---|---|
| LMArena Longer Query | 1482 | 1103 |
Writing & Preference GLM-5.3 leads
GLM-5.3: 75.7 (#6), Mixtral 8x7B: 34.2 (#270)
| Benchmark | GLM-5.3 | Mixtral 8x7B |
|---|---|---|
| LMArena Text | 1471 | 1132 |
| LMArena Creative Writing | 1457 | 1109 |
| LMArena Multi-Turn | 1472 | 1115 |
| EQ-Bench Creative Writing | 2075 | — |
| WildBench | — | 67.3% |
Frequently asked questions
Is GLM-5.3 better than Mixtral 8x7B?
GLM-5.3 is the stronger model overall, scoring 54.8 to 27.1 on the Noometry Index. Mixtral 8x7B costs 3.1× less per token, which makes it the better buy when GLM-5.3's lead doesn't matter for your workload.
Which is cheaper, GLM-5.3 or Mixtral 8x7B?
Mixtral 8x7B is cheaper. It lists at $0.70 per million input tokens and $0.70 per million output tokens; GLM-5.3 lists at $1.40 and $4.40.
Is GLM-5.3 or Mixtral 8x7B better for coding?
GLM-5.3 scores higher on coding benchmarks: 59.5 versus 32.8 in the Noometry coding category.
Which has the bigger context window?
GLM-5.3 does, with 1M tokens against 32K.
How many benchmarks do GLM-5.3 and Mixtral 8x7B share?
20 benchmarks have published results for both models. GLM-5.3 has 42 scored results on Noometry and Mixtral 8x7B has 38.