Model comparison
Grok 4.5 vs Mixtral 8x22B
Grok 4.5 is the stronger model overall, scoring 55.0 to 27.1 on the Noometry Index.
Last verified . 21 shared benchmarks.
Mistral AISummary
- They share 21 benchmarks with published results for both. Grok 4.5 scores higher in 9 categories and Mixtral 8x22B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Grok 4.5 leads 62.3 to 15.1.
- The biggest single-benchmark swing is GPQA Diamond: 93.4% for Grok 4.5 and 34.1% for Mixtral 8x22B.
- Both cost about the same: $2 input and $6 output per million tokens.
- Grok 4.5 accepts more context: 500K tokens versus 64K.
- Mixtral 8x22B has downloadable open weights; the other is API-only.
Side by side
| Grok 4.5 | Mixtral 8x22B | |
|---|---|---|
| Provider | xAI | Mistral AI |
| Noometry Index | 55.0 | 27.1 |
| Released | 2026-07-08 | 2024-04-17 |
| Weights | Proprietary | Open |
| Context window | 500K | 64K |
| Max output | 500K | 64K |
| Input $ / M tokens | $2 | $2 |
| Output $ / M tokens | $6 | $6 |
| Results tracked | 52 | 34 |
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Category by category
Coding Grok 4.5 leads
Grok 4.5: 52.2 (#35), Mixtral 8x22B: 24.2 (#329)
| Benchmark | Grok 4.5 | Mixtral 8x22B |
|---|---|---|
| WeirdML | 46.4% | 3.2% |
| LMArena Coding | 1474 | 1166 |
| DeepSWE | 53.8% | — |
| FrontierCode | 42.4% | — |
| LMArena WebDev | 1553 | — |
| SciCode | 54.1% | — |
| BigCodeBench Instruct | — | 40.6% |
| BigCodeBench Complete | — | 50.2% |
| ALE-Bench | 1,309 | — |
| HumanEval+ | — | 72% |
| MBPP+ | — | 64.3% |
Agentic & Tool Use Grok 4.5 leads
Grok 4.5: 44.4 (#17), Mixtral 8x22B: 23.1 (#127)
| Benchmark | Grok 4.5 | Mixtral 8x22B |
|---|---|---|
| APEX-Agents | 56.2% | — |
| τ²-bench Banking | 47.9% | — |
| Cybench | — | 7.5% |
| PostTrainBench | 23.4% | — |
| GBAEval | 65.4% | — |
| GDP.pdf | 14% | — |
| LMArena Search | 1213 | — |
| Vending-Bench 2 | 3,887 | — |
Reasoning Grok 4.5 leads
Grok 4.5: 56.1 (#25), Mixtral 8x22B: 19.9 (#248)
| Benchmark | Grok 4.5 | Mixtral 8x22B |
|---|---|---|
| LMArena Hard Prompts | 1462 | 1150 |
| DTBench | 96.5% | 55.1% |
| Epoch Capabilities Index | 153.92 | 122.03 |
| ARC-AGI-2 | 52.6% | — |
| SimpleBench | 70% | — |
| Kagi LLM Benchmark | 83.5% | — |
| NYT Connections (extended) | 79.9% | — |
| ARC-AGI-1 | 87.2% | — |
| CritPt | 15.4% | — |
| Chess Puzzles | 36% | — |
| LMCA | 45.2% | — |
| Surface Evolver Bench | 74.4% | — |
| ForecastBench | — | 56.3 |
Math Grok 4.5 leads
Grok 4.5: 60.9 (#35), Mixtral 8x22B: 22.9 (#275)
| Benchmark | Grok 4.5 | Mixtral 8x22B |
|---|---|---|
| LMArena Math | 1459 | 1184 |
| FrontierMath (Tiers 1-3) | 57.2% | — |
| FrontierMath Tier 4 | 24.4% | — |
| OTIS Mock AIME 2024-2025 | 97.8% | — |
| ProofBench | 31% | — |
| Omni-MATH | — | 16.3% |
| MATH Level 5 | — | 24.2% |
Knowledge Grok 4.5 leads
Grok 4.5: 62.3 (#24), Mixtral 8x22B: 15.1 (#293)
| Benchmark | Grok 4.5 | Mixtral 8x22B |
|---|---|---|
| GPQA Diamond | 93.4% | 34.1% |
| LMArena Expert | 1466 | 1113 |
| SimpleQA Verified | 48.3% | — |
| MMLU-Pro | — | 46% |
| GPQA (HELM) | — | 33.4% |
| MMLU | — | 77.8% |
Multimodal Not comparable
Grok 4.5: 37.6 (#72), Mixtral 8x22B: —
| Benchmark | Grok 4.5 | Mixtral 8x22B |
|---|---|---|
| LMArena Vision | 1288 | — |
| Blueprint-Bench 2 | 27.3% | — |
| Furniture Assembly | 22.5% | — |
| LMArena Document | 1452 | — |
Multilingual Grok 4.5 leads
Grok 4.5: 54.4 (#42), Mixtral 8x22B: 32.8 (#255)
| Benchmark | Grok 4.5 | Mixtral 8x22B |
|---|---|---|
| LMArena Non-English | 1440 | 1128 |
| LMArena Chinese | 1496 | 1116 |
| LMArena French | 1456 | 1166 |
| LMArena German | 1446 | 1141 |
| LMArena Japanese | 1428 | 1037 |
| LMArena Korean | 1404 | 1057 |
| LMArena Russian | 1448 | 1158 |
| LMArena Spanish | 1450 | 1151 |
Instruction Following Grok 4.5 leads
Grok 4.5: 76.0 (#48), Mixtral 8x22B: 57.7 (#266)
| Benchmark | Grok 4.5 | Mixtral 8x22B |
|---|---|---|
| LMArena Instruction Following | 1446 | 1147 |
| IFEval | — | 72.4% |
Long Context Grok 4.5 leads
Grok 4.5: 44.8 (#56), Mixtral 8x22B: 34.7 (#247)
| Benchmark | Grok 4.5 | Mixtral 8x22B |
|---|---|---|
| LMArena Longer Query | 1463 | 1144 |
Writing & Preference Grok 4.5 leads
Grok 4.5: 65.8 (#42), Mixtral 8x22B: 36.9 (#262)
| Benchmark | Grok 4.5 | Mixtral 8x22B |
|---|---|---|
| LMArena Text | 1448 | 1162 |
| LMArena Creative Writing | 1442 | 1141 |
| LMArena Multi-Turn | 1456 | 1130 |
| EQ-Bench Creative Writing | 1579 | — |
| WildBench | — | 71.1% |
Frequently asked questions
Is Grok 4.5 better than Mixtral 8x22B?
Grok 4.5 is the stronger model overall, scoring 55.0 to 27.1 on the Noometry Index.
Which is cheaper, Grok 4.5 or Mixtral 8x22B?
Mixtral 8x22B is cheaper. It lists at $2 per million input tokens and $6 per million output tokens; Grok 4.5 lists at $2 and $6.
Is Grok 4.5 or Mixtral 8x22B better for coding?
Grok 4.5 scores higher on coding benchmarks: 52.2 versus 24.2 in the Noometry coding category.
Which has the bigger context window?
Grok 4.5 does, with 500K tokens against 64K.
How many benchmarks do Grok 4.5 and Mixtral 8x22B share?
21 benchmarks have published results for both models. Grok 4.5 has 52 scored results on Noometry and Mixtral 8x22B has 34.