Model comparison
GPT-5.6 Terra vs Mistral Large 3
GPT-5.6 Terra is the stronger model overall, scoring 59.2 to 39.1 on the Noometry Index. Mistral Large 3 costs 12× less per token, which makes it the better buy when GPT-5.6 Terra's lead doesn't matter for your workload.
Last verified . 22 shared benchmarks.
Mistral AISummary
- They share 22 benchmarks with published results for both. GPT-5.6 Terra scores higher in 9 categories and Mistral Large 3 in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.6 Terra leads 60.7 to 15.2.
- The biggest single-benchmark swing is NYT Connections (extended): 78.4% for GPT-5.6 Terra and 7.5% for Mistral Large 3.
- Mistral Large 3 is cheaper at $0.25 / $0.75 per million input/output tokens, against $2 / $12 for GPT-5.6 Terra.
- GPT-5.6 Terra accepts more context: 1.05M tokens versus 262K.
- Mistral Large 3 has downloadable open weights; the other is API-only.
Side by side
| GPT-5.6 Terra | Mistral Large 3 | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 59.2 | 39.1 |
| Released | 2026-07-09 | 2025-12-02 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 262K |
| Max output | 128K | 8K |
| Input $ / M tokens | $2 | $0.25 |
| Output $ / M tokens | $12 | $0.75 |
| Results tracked | 52 | 24 |
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Category by category
Coding GPT-5.6 Terra leads
GPT-5.6 Terra: 57.7 (#19), Mistral Large 3: 34.4 (#237)
| Benchmark | GPT-5.6 Terra | Mistral Large 3 |
|---|---|---|
| LMArena WebDev | 1522 | 1230 |
| LMArena Coding | 1484 | 1448 |
| DeepSWE | 69.6% | — |
| FrontierCode | 41.3% | — |
| CursorBench | 41.3% | — |
| SciCode | 55% | — |
| WeirdML | 78.3% | — |
| ALE-Bench | 1,951 | — |
Agentic & Tool Use Not comparable
GPT-5.6 Terra: 40.1 (#25), Mistral Large 3: —
| Benchmark | GPT-5.6 Terra | Mistral Large 3 |
|---|---|---|
| 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: 60.7 (#21), Mistral Large 3: 15.2 (#319)
| Benchmark | GPT-5.6 Terra | Mistral Large 3 |
|---|---|---|
| Kagi LLM Benchmark | 51.3% | 50.9% |
| NYT Connections (extended) | 78.4% | 7.5% |
| LMArena Hard Prompts | 1468 | 1429 |
| ARC-AGI-2 | 83.9% | — |
| SimpleBench | 48.9% | — |
| ARC-AGI-1 | 96.5% | — |
| CritPt | 30% | — |
| Chess Puzzles | 54% | — |
| Thematic Generalization | — | 23% |
| Mystery Game Puzzles | 35% | — |
| DTBench | 93.3% | — |
| LMCA | 55% | — |
| Surface Evolver Bench | 83.8% | — |
| Epoch Capabilities Index | 159.62 | — |
Math GPT-5.6 Terra leads
GPT-5.6 Terra: 81.6 (#12), Mistral Large 3: 38.7 (#129)
| Benchmark | GPT-5.6 Terra | Mistral Large 3 |
|---|---|---|
| LMArena Math | 1466 | 1414 |
| FrontierMath (Tiers 1-3) | 86% | — |
| FrontierMath Tier 4 | 70.7% | — |
| OTIS Mock AIME 2024-2025 | 99.7% | — |
| ProofBench | 74% | — |
Knowledge GPT-5.6 Terra leads
GPT-5.6 Terra: 61.2 (#30), Mistral Large 3: 36.0 (#177)
| Benchmark | GPT-5.6 Terra | Mistral Large 3 |
|---|---|---|
| LMArena Expert | 1492 | 1421 |
| GPQA Diamond | 93.3% | — |
| SimpleQA Verified | 43.2% | — |
| Vectara Hallucination Rate | — | 14.5% |
Multimodal GPT-5.6 Terra leads
GPT-5.6 Terra: 47.3 (#11), Mistral Large 3: 38.2 (#66)
| Benchmark | GPT-5.6 Terra | Mistral Large 3 |
|---|---|---|
| LMArena Vision | 1271 | 1221 |
| Blueprint-Bench 2 | 30.8% | — |
| Furniture Assembly | 54.2% | — |
| LMArena Document | 1472 | — |
Multilingual GPT-5.6 Terra leads
GPT-5.6 Terra: 54.4 (#44), Mistral Large 3: 52.5 (#84)
| Benchmark | GPT-5.6 Terra | Mistral Large 3 |
|---|---|---|
| LMArena Non-English | 1439 | 1413 |
| LMArena Chinese | 1513 | 1447 |
| LMArena French | 1471 | 1455 |
| LMArena German | 1460 | 1437 |
| LMArena Japanese | 1457 | 1394 |
| LMArena Korean | 1425 | 1384 |
| LMArena Russian | 1450 | 1411 |
| LMArena Spanish | 1448 | 1440 |
Instruction Following GPT-5.6 Terra leads
GPT-5.6 Terra: 76.4 (#40), Mistral Large 3: 74.0 (#108)
| Benchmark | GPT-5.6 Terra | Mistral Large 3 |
|---|---|---|
| LMArena Instruction Following | 1454 | 1403 |
Long Context GPT-5.6 Terra leads
GPT-5.6 Terra: 44.4 (#68), Mistral Large 3: 43.1 (#105)
| Benchmark | GPT-5.6 Terra | Mistral Large 3 |
|---|---|---|
| LMArena Longer Query | 1451 | 1413 |
Writing & Preference GPT-5.6 Terra leads
GPT-5.6 Terra: 70.2 (#23), Mistral Large 3: 60.0 (#101)
| Benchmark | GPT-5.6 Terra | Mistral Large 3 |
|---|---|---|
| LMArena Text | 1447 | 1428 |
| LMArena Creative Writing | 1410 | 1386 |
| EQ-Bench Creative Writing | 1855 | 1412 |
| LMArena Multi-Turn | 1449 | 1429 |
| EQ-Bench 4 | 1234 | — |
Frequently asked questions
Is GPT-5.6 Terra better than Mistral Large 3?
GPT-5.6 Terra is the stronger model overall, scoring 59.2 to 39.1 on the Noometry Index. Mistral Large 3 costs 12× 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 Large 3?
Mistral Large 3 is cheaper. It lists at $0.25 per million input tokens and $0.75 per million output tokens; GPT-5.6 Terra lists at $2 and $12.
Is GPT-5.6 Terra or Mistral Large 3 better for coding?
GPT-5.6 Terra scores higher on coding benchmarks: 57.7 versus 34.4 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Terra does, with 1.05M tokens against 262K.
How many benchmarks do GPT-5.6 Terra and Mistral Large 3 share?
22 benchmarks have published results for both models. GPT-5.6 Terra has 52 scored results on Noometry and Mistral Large 3 has 24.