Model comparison
GPT-5.6 Terra vs Mistral 7B
GPT-5.6 Terra is the stronger model overall, scoring 59.1 to 22.9 on the Noometry Index. Mistral 7B costs 18× less per token, which makes it the better buy when GPT-5.6 Terra'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-5.6 Terra scores higher in 8 categories and Mistral 7B 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 7.6.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 99.7% for GPT-5.6 Terra and 0.3% for Mistral 7B.
- Mistral 7B is cheaper at $0.25 / $0.25 per million input/output tokens, against $2 / $12 for GPT-5.6 Terra.
- GPT-5.6 Terra accepts more context: 1.05M tokens versus 8K.
- Mistral 7B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.6 Terra | Mistral 7B | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 59.1 | 22.9 |
| Released | 2026-07-09 | 2023-09-27 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 8K |
| Max output | 128K | 8K |
| Input $ / M tokens | $2 | $0.25 |
| Output $ / M tokens | $12 | $0.25 |
| Results tracked | 52 | 37 |
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Category by category
Coding GPT-5.6 Terra leads
GPT-5.6 Terra: 57.1 (#16), Mistral 7B: 25.9 (#321)
| Benchmark | GPT-5.6 Terra | Mistral 7B |
|---|---|---|
| LMArena Coding | 1484 | 1082 |
| DeepSWE | 69.6% | — |
| FrontierCode | 41.3% | — |
| CursorBench | 41.3% | — |
| LMArena WebDev | 1520 | — |
| SciCode | 55% | — |
| WeirdML | 78.3% | — |
| BigCodeBench Instruct | — | 19.5% |
| BigCodeBench Complete | — | 27.3% |
| ALE-Bench | 1,951 | — |
| HumanEval+ | — | 36% |
| MBPP+ | — | 42.1% |
Agentic & Tool Use Not comparable
GPT-5.6 Terra: 40.2 (#23), Mistral 7B: —
| Benchmark | GPT-5.6 Terra | Mistral 7B |
|---|---|---|
| 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), Mistral 7B: 12.6 (#331)
| Benchmark | GPT-5.6 Terra | Mistral 7B |
|---|---|---|
| Chess Puzzles | 54% | 0% |
| LMArena Hard Prompts | 1467 | 1067 |
| DTBench | 93.3% | 42.5% |
| Epoch Capabilities Index | 159.31 | 111.97 |
| 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% | — |
| Mystery Game Puzzles | 35% | — |
| LMCA | 55% | — |
| Surface Evolver Bench | 83.8% | — |
| Adversarial NLI | — | 47.1% |
| BIG-Bench Hard | — | 56.1% |
| HellaSwag | — | 81% |
| PIQA | — | 83% |
| WinoGrande | — | 75.3% |
Math GPT-5.6 Terra leads
GPT-5.6 Terra: 81.5 (#10), Mistral 7B: 7.6 (#319)
| Benchmark | GPT-5.6 Terra | Mistral 7B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 99.7% | 0.3% |
| LMArena Math | 1463 | 1085 |
| FrontierMath (Tiers 1-3) | 86% | — |
| FrontierMath Tier 4 | 70.7% | — |
| ProofBench | 74% | — |
| MATH Level 5 | — | 3.7% |
| GSM8K | — | 54.4% |
Knowledge GPT-5.6 Terra leads
GPT-5.6 Terra: 61.3 (#27), Mistral 7B: 7.3 (#306)
| Benchmark | GPT-5.6 Terra | Mistral 7B |
|---|---|---|
| GPQA Diamond | 93.3% | 15.2% |
| LMArena Expert | 1492 | 1036 |
| SimpleQA Verified | 43.2% | — |
| ARC (AI2) Challenge | — | 78.6% |
| BoolQ | — | 87.4% |
| MMLU | — | 62.5% |
| OpenBookQA | — | 79.8% |
| TriviaQA | — | 75.2% |
Multimodal Not comparable
GPT-5.6 Terra: 47.5 (#10), Mistral 7B: —
| Benchmark | GPT-5.6 Terra | Mistral 7B |
|---|---|---|
| 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), Mistral 7B: 26.0 (#278)
| Benchmark | GPT-5.6 Terra | Mistral 7B |
|---|---|---|
| LMArena Non-English | 1439 | 1012 |
| LMArena Chinese | 1514 | 1009 |
| LMArena French | 1476 | 1036 |
| LMArena German | 1462 | 987 |
| LMArena Japanese | 1446 | 878 |
| LMArena Russian | 1450 | 1018 |
| LMArena Spanish | 1455 | 1026 |
| LMArena Korean | 1420 | — |
Instruction Following GPT-5.6 Terra leads
GPT-5.6 Terra: 76.6 (#38), Mistral 7B: 54.5 (#275)
| Benchmark | GPT-5.6 Terra | Mistral 7B |
|---|---|---|
| LMArena Instruction Following | 1453 | 1060 |
Long Context GPT-5.6 Terra leads
GPT-5.6 Terra: 44.4 (#64), Mistral 7B: 32.2 (#266)
| Benchmark | GPT-5.6 Terra | Mistral 7B |
|---|---|---|
| LMArena Longer Query | 1452 | 1060 |
Writing & Preference GPT-5.6 Terra leads
GPT-5.6 Terra: 70.4 (#21), Mistral 7B: 30.9 (#281)
| Benchmark | GPT-5.6 Terra | Mistral 7B |
|---|---|---|
| LMArena Text | 1446 | 1090 |
| LMArena Creative Writing | 1411 | 1068 |
| LMArena Multi-Turn | 1451 | 1062 |
| EQ-Bench Creative Writing | 1855 | — |
| EQ-Bench 4 | 1234 | — |
Frequently asked questions
Is GPT-5.6 Terra better than Mistral 7B?
GPT-5.6 Terra is the stronger model overall, scoring 59.1 to 22.9 on the Noometry Index. Mistral 7B costs 18× 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 Mistral 7B?
Mistral 7B is cheaper. It lists at $0.25 per million input tokens and $0.25 per million output tokens; GPT-5.6 Terra lists at $2 and $12.
Is GPT-5.6 Terra or Mistral 7B better for coding?
GPT-5.6 Terra scores higher on coding benchmarks: 57.1 versus 25.9 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Terra does, with 1.05M tokens against 8K.
How many benchmarks do GPT-5.6 Terra and Mistral 7B share?
21 benchmarks have published results for both models. GPT-5.6 Terra has 52 scored results on Noometry and Mistral 7B has 37.