Model comparison
Grok 4.6 vs Mixtral 8x7B
Grok 4.6 is the stronger model overall, scoring 56.9 to 27.1 on the Noometry Index. Mixtral 8x7B costs 4.3× less per token, which makes it the better buy when Grok 4.6's lead doesn't matter for your workload.
Last verified . 20 shared benchmarks.
Mistral AISummary
- They share 20 benchmarks with published results for both. Grok 4.6 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 Grok 4.6 leads 63.3 to 11.0.
- The biggest single-benchmark swing is GPQA Diamond: 94% for Grok 4.6 and 30.6% for Mixtral 8x7B.
- Mixtral 8x7B is cheaper at $0.70 / $0.70 per million input/output tokens, against $2 / $6 for Grok 4.6.
- Grok 4.6 accepts more context: 500K tokens versus 32K.
- Mixtral 8x7B has downloadable open weights; the other is API-only.
Side by side
| Grok 4.6 | Mixtral 8x7B | |
|---|---|---|
| Provider | xAI | Mistral AI |
| Noometry Index | 56.9 | 27.1 |
| Released | 2026-08-12 | 2023-12-11 |
| Weights | Proprietary | Open |
| Context window | 500K | 32K |
| Max output | 500K | 32K |
| Input $ / M tokens | $2 | $0.70 |
| Output $ / M tokens | $6 | $0.70 |
| Results tracked | 49 | 38 |
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Category by category
Coding Grok 4.6 leads
Grok 4.6: 58.5 (#16), Mixtral 8x7B: 32.8 (#269)
| Benchmark | Grok 4.6 | Mixtral 8x7B |
|---|---|---|
| LMArena Coding | 1465 | 1126 |
| DeepSWE | 67.5% | — |
| FrontierCode | 48% | — |
| CursorBench | 41.4% | — |
| LMArena WebDev | 1617 | — |
| FrontierSWE | 25.3% | — |
| SciCode | 56.5% | — |
| WeirdML | 67.3% | — |
| ALE-Bench | 1,508 | — |
| HumanEval+ | — | 39.6% |
| MBPP+ | — | 49.7% |
Agentic & Tool Use Not comparable
Grok 4.6: 39.4 (#27), Mixtral 8x7B: —
| Benchmark | Grok 4.6 | Mixtral 8x7B |
|---|---|---|
| APEX-Agents | 65.3% | — |
| GDP.pdf | 17.2% | — |
| Vending-Bench 2 | 9,047 | — |
Reasoning Grok 4.6 leads
Grok 4.6: 61.4 (#20), Mixtral 8x7B: 18.2 (#285)
| Benchmark | Grok 4.6 | Mixtral 8x7B |
|---|---|---|
| LMArena Hard Prompts | 1447 | 1115 |
| DTBench | 97.3% | 49.6% |
| Epoch Capabilities Index | 156.44 | 118.47 |
| ARC-AGI-2 | 67.1% | — |
| SimpleBench | 75.9% | — |
| NYT Connections (extended) | 80% | — |
| ARC-AGI-1 | 87.5% | — |
| CritPt | 19.7% | — |
| Chess Puzzles | 40% | — |
| EBR-Bench | 30.5% | — |
| Mystery Game Puzzles | 34% | — |
| LMCA | 48.5% | — |
| Adversarial NLI | — | 55.2% |
| ForecastBench | — | 56.3 |
| HellaSwag | — | 86.7% |
| PIQA | — | 83.6% |
| WinoGrande | — | 77.2% |
Math Grok 4.6 leads
Grok 4.6: 67.0 (#24), Mixtral 8x7B: 18.8 (#289)
| Benchmark | Grok 4.6 | Mixtral 8x7B |
|---|---|---|
| LMArena Math | 1423 | 1147 |
| FrontierMath (Tiers 1-3) | 66% | — |
| FrontierMath Tier 4 | 31.7% | — |
| OTIS Mock AIME 2024-2025 | 99.2% | — |
| ProofBench | 51% | — |
| Omni-MATH | — | 10.5% |
| MATH Level 5 | — | 10% |
| GSM8K | — | 74.4% |
Knowledge Grok 4.6 leads
Grok 4.6: 63.3 (#20), Mixtral 8x7B: 11.0 (#301)
| Benchmark | Grok 4.6 | Mixtral 8x7B |
|---|---|---|
| GPQA Diamond | 94% | 30.6% |
| LMArena Expert | 1467 | 1088 |
| SimpleQA Verified | 49.3% | — |
| MMLU-Pro | — | 33.5% |
| GPQA (HELM) | — | 29.6% |
| ARC (AI2) Challenge | — | 87.3% |
| MMLU | — | 70.6% |
| OpenBookQA | — | 85.8% |
| TriviaQA | — | 82.2% |
Multimodal Not comparable
Grok 4.6: 43.6 (#23), Mixtral 8x7B: —
| Benchmark | Grok 4.6 | Mixtral 8x7B |
|---|---|---|
| LMArena Vision | 1263 | — |
| Blueprint-Bench 2 | 33.2% | — |
| Furniture Assembly | 40% | — |
| LMArena Document | 1452 | — |
Multilingual Grok 4.6 leads
Grok 4.6: 53.0 (#74), Mixtral 8x7B: 29.6 (#266)
| Benchmark | Grok 4.6 | Mixtral 8x7B |
|---|---|---|
| LMArena Non-English | 1420 | 1077 |
| LMArena Chinese | 1480 | 1055 |
| LMArena French | 1461 | 1166 |
| LMArena German | 1431 | 1114 |
| LMArena Japanese | 1376 | 931 |
| LMArena Korean | 1397 | 968 |
| LMArena Russian | 1422 | 1090 |
| LMArena Spanish | 1404 | 1111 |
Instruction Following Grok 4.6 leads
Grok 4.6: 75.4 (#63), Mixtral 8x7B: 51.0 (#297)
| Benchmark | Grok 4.6 | Mixtral 8x7B |
|---|---|---|
| LMArena Instruction Following | 1431 | 1109 |
| IFEval | — | 57.5% |
Long Context Grok 4.6 leads
Grok 4.6: 44.5 (#66), Mixtral 8x7B: 33.4 (#260)
| Benchmark | Grok 4.6 | Mixtral 8x7B |
|---|---|---|
| LMArena Longer Query | 1454 | 1103 |
Writing & Preference Grok 4.6 leads
Grok 4.6: 62.3 (#80), Mixtral 8x7B: 34.2 (#270)
| Benchmark | Grok 4.6 | Mixtral 8x7B |
|---|---|---|
| LMArena Text | 1428 | 1132 |
| LMArena Creative Writing | 1428 | 1109 |
| LMArena Multi-Turn | 1425 | 1115 |
| WildBench | — | 67.3% |
Frequently asked questions
Is Grok 4.6 better than Mixtral 8x7B?
Grok 4.6 is the stronger model overall, scoring 56.9 to 27.1 on the Noometry Index. Mixtral 8x7B costs 4.3× less per token, which makes it the better buy when Grok 4.6's lead doesn't matter for your workload.
Which is cheaper, Grok 4.6 or Mixtral 8x7B?
Mixtral 8x7B is cheaper. It lists at $0.70 per million input tokens and $0.70 per million output tokens; Grok 4.6 lists at $2 and $6.
Is Grok 4.6 or Mixtral 8x7B better for coding?
Grok 4.6 scores higher on coding benchmarks: 58.5 versus 32.8 in the Noometry coding category.
Which has the bigger context window?
Grok 4.6 does, with 500K tokens against 32K.
How many benchmarks do Grok 4.6 and Mixtral 8x7B share?
20 benchmarks have published results for both models. Grok 4.6 has 49 scored results on Noometry and Mixtral 8x7B has 38.