Model comparison
GPT-6 Astra vs Mixtral 8x22B
GPT-6 Astra is the stronger model overall, scoring 70.8 to 27.1 on the Noometry Index. Mixtral 8x22B costs 6.7× less per token, which makes it the better buy when GPT-6 Astra's lead doesn't matter for your workload.
Last verified . 21 shared benchmarks.
Mistral AISummary
- They share 21 benchmarks with published results for both. GPT-6 Astra scores higher in 9 categories and Mixtral 8x22B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-6 Astra leads 93.5 to 22.9.
- The biggest single-benchmark swing is WeirdML: 93.6% for GPT-6 Astra and 3.2% for Mixtral 8x22B.
- Mixtral 8x22B is cheaper at $2 / $6 per million input/output tokens, against $10 / $50 for GPT-6 Astra.
- GPT-6 Astra accepts more context: 1.05M tokens versus 64K.
- Mixtral 8x22B has downloadable open weights; the other is API-only.
Side by side
| GPT-6 Astra | Mixtral 8x22B | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 70.8 | 27.1 |
| Released | 2026-09-03 | 2024-04-17 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 64K |
| Max output | 128K | 64K |
| Input $ / M tokens | $10 | $2 |
| Output $ / M tokens | $50 | $6 |
| Results tracked | 56 | 34 |
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Category by category
Coding GPT-6 Astra leads
GPT-6 Astra: 73.7 (#2), Mixtral 8x22B: 24.2 (#329)
| Benchmark | GPT-6 Astra | Mixtral 8x22B |
|---|---|---|
| WeirdML | 93.6% | 3.2% |
| LMArena Coding | 1487 | 1166 |
| DeepSWE | 74.1% | — |
| FrontierCode | 53.3% | — |
| LMArena WebDev | 1786 | — |
| FrontierSWE | 65.5% | — |
| SciCode | 56.5% | — |
| GSO | 79.4% | — |
| BigCodeBench Instruct | — | 40.6% |
| MirrorCode | 46.7% | — |
| BigCodeBench Complete | — | 50.2% |
| ALE-Bench | 2,951 | — |
| HumanEval+ | — | 72% |
| MBPP+ | — | 64.3% |
Agentic & Tool Use GPT-6 Astra leads
GPT-6 Astra: 52.9 (#3), Mixtral 8x22B: 23.1 (#127)
| Benchmark | GPT-6 Astra | Mixtral 8x22B |
|---|---|---|
| APEX-Agents | 64.7% | — |
| Remote Labor Index | 20.8% | — |
| Cybench | — | 7.5% |
| BALROG | 68.3% | — |
| GDP.pdf | 34.2% | — |
| Vending-Bench 2 | 15,515 | — |
Reasoning GPT-6 Astra leads
GPT-6 Astra: 85.1 (#1), Mixtral 8x22B: 19.9 (#248)
| Benchmark | GPT-6 Astra | Mixtral 8x22B |
|---|---|---|
| LMArena Hard Prompts | 1462 | 1150 |
| DTBench | 97.3% | 55.1% |
| Epoch Capabilities Index | 166.45 | 122.03 |
| ARC-AGI-2 | 95% | — |
| NYT Connections (extended) | 98.1% | — |
| ARC-AGI-1 | 98.5% | — |
| CritPt | 31.7% | — |
| Chess Puzzles | 72% | — |
| EBR-Bench | 76.2% | — |
| Mystery Game Puzzles | 84% | — |
| LMCA | 64.4% | — |
| Bench to the Future 3 | 0.14 | — |
| ForecastBench | — | 56.3 |
Math GPT-6 Astra leads
GPT-6 Astra: 93.5 (#2), Mixtral 8x22B: 22.9 (#275)
| Benchmark | GPT-6 Astra | Mixtral 8x22B |
|---|---|---|
| LMArena Math | 1465 | 1184 |
| FrontierMath (Tiers 1-3) | 93.7% | — |
| FrontierMath Tier 4 | 97.6% | — |
| OTIS Mock AIME 2024-2025 | 100% | — |
| ProofBench | 99% | — |
| Omni-MATH | — | 16.3% |
| MATH Level 5 | — | 24.2% |
| FrontierMath Erdős | 2.9% | — |
Knowledge GPT-6 Astra leads
GPT-6 Astra: 75.3 (#1), Mixtral 8x22B: 15.1 (#293)
| Benchmark | GPT-6 Astra | Mixtral 8x22B |
|---|---|---|
| GPQA Diamond | 95.8% | 34.1% |
| LMArena Expert | 1483 | 1113 |
| Humanity's Last Exam | 54.8% | — |
| SimpleQA Verified | 75.6% | — |
| MMLU-Pro | — | 46% |
| Vectara Hallucination Rate | 8.7% | — |
| GPQA (HELM) | — | 33.4% |
| MMLU | — | 77.8% |
Multimodal Not comparable
GPT-6 Astra: 55.0 (#3), Mixtral 8x22B: —
| Benchmark | GPT-6 Astra | Mixtral 8x22B |
|---|---|---|
| LMArena Vision | 1281 | — |
| Blueprint-Bench 2 | 49.7% | — |
| Furniture Assembly | 80% | — |
| LMArena Document | 1468 | — |
Multilingual GPT-6 Astra leads
GPT-6 Astra: 53.7 (#61), Mixtral 8x22B: 32.8 (#255)
| Benchmark | GPT-6 Astra | Mixtral 8x22B |
|---|---|---|
| LMArena Non-English | 1430 | 1128 |
| LMArena Chinese | 1484 | 1116 |
| LMArena French | 1456 | 1166 |
| LMArena German | 1440 | 1141 |
| LMArena Japanese | 1379 | 1037 |
| LMArena Korean | 1426 | 1057 |
| LMArena Russian | 1436 | 1158 |
| LMArena Spanish | 1407 | 1151 |
Instruction Following GPT-6 Astra leads
GPT-6 Astra: 76.3 (#44), Mixtral 8x22B: 57.7 (#266)
| Benchmark | GPT-6 Astra | Mixtral 8x22B |
|---|---|---|
| LMArena Instruction Following | 1450 | 1147 |
| IFEval | — | 72.4% |
Long Context GPT-6 Astra leads
GPT-6 Astra: 44.5 (#62), Mixtral 8x22B: 34.7 (#247)
| Benchmark | GPT-6 Astra | Mixtral 8x22B |
|---|---|---|
| LMArena Longer Query | 1456 | 1144 |
Writing & Preference GPT-6 Astra leads
GPT-6 Astra: 75.3 (#7), Mixtral 8x22B: 36.9 (#262)
| Benchmark | GPT-6 Astra | Mixtral 8x22B |
|---|---|---|
| LMArena Text | 1441 | 1162 |
| LMArena Creative Writing | 1418 | 1141 |
| LMArena Multi-Turn | 1448 | 1130 |
| EQ-Bench Creative Writing | 2173 | — |
| WildBench | — | 71.1% |
Frequently asked questions
Is GPT-6 Astra better than Mixtral 8x22B?
GPT-6 Astra is the stronger model overall, scoring 70.8 to 27.1 on the Noometry Index. Mixtral 8x22B costs 6.7× less per token, which makes it the better buy when GPT-6 Astra's lead doesn't matter for your workload.
Which is cheaper, GPT-6 Astra or Mixtral 8x22B?
Mixtral 8x22B is cheaper. It lists at $2 per million input tokens and $6 per million output tokens; GPT-6 Astra lists at $10 and $50.
Is GPT-6 Astra or Mixtral 8x22B better for coding?
GPT-6 Astra scores higher on coding benchmarks: 73.7 versus 24.2 in the Noometry coding category.
Which has the bigger context window?
GPT-6 Astra does, with 1.05M tokens against 64K.
How many benchmarks do GPT-6 Astra and Mixtral 8x22B share?
21 benchmarks have published results for both models. GPT-6 Astra has 56 scored results on Noometry and Mixtral 8x22B has 34.