Model comparison
GPT-5.6 Terra vs Mixtral 8x7B
GPT-5.6 Terra is the stronger model overall, scoring 59.1 to 26.8 on the Noometry Index. Mixtral 8x7B costs 6.4× less per token, which makes it the better buy when GPT-5.6 Terra's lead doesn't matter for your workload.
Last verified . 20 shared benchmarks.
Mistral AISummary
- They share 20 benchmarks with published results for both. GPT-5.6 Terra scores higher in 8 categories and Mixtral 8x7B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Terra leads 81.5 to 17.2.
- The biggest single-benchmark swing is GPQA Diamond: 93.3% for GPT-5.6 Terra and 30.6% for Mixtral 8x7B.
- Mixtral 8x7B is cheaper at $0.70 / $0.70 per million input/output tokens, against $2 / $12 for GPT-5.6 Terra.
- GPT-5.6 Terra accepts more context: 1.05M tokens versus 32K.
- Mixtral 8x7B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.6 Terra | Mixtral 8x7B | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 59.1 | 26.8 |
| Released | 2026-07-09 | 2023-12-11 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 32K |
| Max output | 128K | 32K |
| Input $ / M tokens | $2 | $0.70 |
| Output $ / M tokens | $12 | $0.70 |
| Results tracked | 52 | 38 |
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Category by category
Coding GPT-5.6 Terra leads
GPT-5.6 Terra: 57.1 (#16), Mixtral 8x7B: 32.2 (#265)
| Benchmark | GPT-5.6 Terra | Mixtral 8x7B |
|---|---|---|
| LMArena Coding | 1484 | 1127 |
| DeepSWE | 69.6% | — |
| FrontierCode | 41.3% | — |
| CursorBench | 41.3% | — |
| LMArena WebDev | 1520 | — |
| SciCode | 55% | — |
| WeirdML | 78.3% | — |
| ALE-Bench | 1,951 | — |
| HumanEval+ | — | 39.6% |
| MBPP+ | — | 49.7% |
Agentic & Tool Use Not comparable
GPT-5.6 Terra: 40.2 (#23), Mixtral 8x7B: —
| Benchmark | GPT-5.6 Terra | Mixtral 8x7B |
|---|---|---|
| APEX-Agents | 58.2% | — |
| BALROG | 53.2% | — |
| GDP.pdf | 24.7% | — |
| Vending-Bench 2 | 7,343 | — |
Reasoning GPT-5.6 Terra leads
GPT-5.6 Terra: 60.6 (#19), Mixtral 8x7B: 17.4 (#283)
| Benchmark | GPT-5.6 Terra | Mixtral 8x7B |
|---|---|---|
| LMArena Hard Prompts | 1467 | 1115 |
| DTBench | 93.3% | 49.6% |
| Epoch Capabilities Index | 159.31 | 118.38 |
| ARC-AGI-2 | 83.9% | — |
| SimpleBench | 48.9% | — |
| Kagi LLM Benchmark | 51.3% | — |
| NYT Connections (extended) | 78.4% | — |
| ARC-AGI-1 | 96.5% | — |
| CritPt | 30% | — |
| Chess Puzzles | 54% | — |
| Mystery Game Puzzles | 35% | — |
| LMCA | 55% | — |
| Surface Evolver Bench | 83.8% | — |
| Adversarial NLI | — | 55.2% |
| ForecastBench | — | 56.3 |
| HellaSwag | — | 86.7% |
| PIQA | — | 83.6% |
| WinoGrande | — | 77.2% |
Math GPT-5.6 Terra leads
GPT-5.6 Terra: 81.5 (#10), Mixtral 8x7B: 17.2 (#284)
| Benchmark | GPT-5.6 Terra | Mixtral 8x7B |
|---|---|---|
| LMArena Math | 1463 | 1147 |
| FrontierMath (Tiers 1-3) | 86% | — |
| FrontierMath Tier 4 | 70.7% | — |
| OTIS Mock AIME 2024-2025 | 99.7% | — |
| ProofBench | 74% | — |
| Omni-MATH | — | 10.5% |
| MATH Level 5 | — | 10% |
| GSM8K | — | 74.4% |
Knowledge GPT-5.6 Terra leads
GPT-5.6 Terra: 61.3 (#27), Mixtral 8x7B: 11.0 (#296)
| Benchmark | GPT-5.6 Terra | Mixtral 8x7B |
|---|---|---|
| GPQA Diamond | 93.3% | 30.6% |
| LMArena Expert | 1492 | 1088 |
| SimpleQA Verified | 43.2% | — |
| 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
GPT-5.6 Terra: 47.5 (#10), Mixtral 8x7B: —
| Benchmark | GPT-5.6 Terra | Mixtral 8x7B |
|---|---|---|
| LMArena Vision | 1271 | — |
| Blueprint-Bench 2 | 30.8% | — |
| Furniture Assembly | 54.2% | — |
| LMArena Document | 1472 | — |
Multilingual GPT-5.6 Terra leads
GPT-5.6 Terra: 54.5 (#42), Mixtral 8x7B: 29.8 (#261)
| Benchmark | GPT-5.6 Terra | Mixtral 8x7B |
|---|---|---|
| LMArena Non-English | 1439 | 1077 |
| LMArena Chinese | 1514 | 1055 |
| LMArena French | 1476 | 1165 |
| LMArena German | 1462 | 1114 |
| LMArena Japanese | 1446 | 931 |
| LMArena Korean | 1420 | 968 |
| LMArena Russian | 1450 | 1090 |
| LMArena Spanish | 1455 | 1110 |
Instruction Following GPT-5.6 Terra leads
GPT-5.6 Terra: 76.6 (#38), Mixtral 8x7B: 51.2 (#292)
| Benchmark | GPT-5.6 Terra | Mixtral 8x7B |
|---|---|---|
| LMArena Instruction Following | 1453 | 1110 |
| IFEval | — | 57.5% |
Long Context GPT-5.6 Terra leads
GPT-5.6 Terra: 44.4 (#64), Mixtral 8x7B: 33.4 (#255)
| Benchmark | GPT-5.6 Terra | Mixtral 8x7B |
|---|---|---|
| LMArena Longer Query | 1452 | 1103 |
Writing & Preference GPT-5.6 Terra leads
GPT-5.6 Terra: 70.4 (#21), Mixtral 8x7B: 34.4 (#265)
| Benchmark | GPT-5.6 Terra | Mixtral 8x7B |
|---|---|---|
| LMArena Text | 1446 | 1132 |
| LMArena Creative Writing | 1411 | 1109 |
| LMArena Multi-Turn | 1451 | 1115 |
| EQ-Bench Creative Writing | 1855 | — |
| WildBench | — | 67.3% |
| EQ-Bench 4 | 1234 | — |
Frequently asked questions
Is GPT-5.6 Terra better than Mixtral 8x7B?
GPT-5.6 Terra is the stronger model overall, scoring 59.1 to 26.8 on the Noometry Index. Mixtral 8x7B costs 6.4× less per token, which makes it the better buy when GPT-5.6 Terra's lead doesn't matter for your workload.
Which is cheaper, GPT-5.6 Terra or Mixtral 8x7B?
Mixtral 8x7B is cheaper. It lists at $0.70 per million input tokens and $0.70 per million output tokens; GPT-5.6 Terra lists at $2 and $12.
Is GPT-5.6 Terra or Mixtral 8x7B better for coding?
GPT-5.6 Terra scores higher on coding benchmarks: 57.1 versus 32.2 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Terra does, with 1.05M tokens against 32K.
How many benchmarks do GPT-5.6 Terra and Mixtral 8x7B share?
20 benchmarks have published results for both models. GPT-5.6 Terra has 52 scored results on Noometry and Mixtral 8x7B has 38.