Model comparison
GPT-5.6 Terra vs MiMo-V2.5-Pro
GPT-5.6 Terra is the stronger model overall, scoring 59.1 to 44.8 on the Noometry Index. MiMo-V2.5-Pro costs 8.3× less per token, which makes it the better buy when GPT-5.6 Terra's lead doesn't matter for your workload.
Last verified . 27 shared benchmarks.
Summary
- They share 27 benchmarks with published results for both. GPT-5.6 Terra scores higher in 5 categories and MiMo-V2.5-Pro in 3 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Terra leads 81.5 to 38.6.
- The biggest single-benchmark swing is ProofBench: 74% for GPT-5.6 Terra and 22% for MiMo-V2.5-Pro.
- MiMo-V2.5-Pro is cheaper at $0.43 / $0.87 per million input/output tokens, against $2 / $12 for GPT-5.6 Terra.
- GPT-5.6 Terra accepts more context: 1.05M tokens versus 1.05M.
- MiMo-V2.5-Pro has downloadable open weights; the other is API-only.
Side by side
| GPT-5.6 Terra | MiMo-V2.5-Pro | |
|---|---|---|
| Provider | OpenAI | Xiaomi |
| Noometry Index | 59.1 | 44.8 |
| Released | 2026-07-09 | 2026-04-22 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 1.05M |
| Max output | 128K | 131K |
| Input $ / M tokens | $2 | $0.43 |
| Output $ / M tokens | $12 | $0.87 |
| Results tracked | 52 | 27 |
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Category by category
Coding GPT-5.6 Terra leads
GPT-5.6 Terra: 57.1 (#16), MiMo-V2.5-Pro: 47.0 (#58)
| Benchmark | GPT-5.6 Terra | MiMo-V2.5-Pro |
|---|---|---|
| LMArena WebDev | 1520 | 1477 |
| SciCode | 55% | 50.2% |
| LMArena Coding | 1484 | 1502 |
| ALE-Bench | 1,951 | 899.8 |
| DeepSWE | 69.6% | — |
| FrontierCode | 41.3% | — |
| CursorBench | 41.3% | — |
| WeirdML | 78.3% | — |
Agentic & Tool Use Not comparable
GPT-5.6 Terra: 40.2 (#23), MiMo-V2.5-Pro: —
| Benchmark | GPT-5.6 Terra | MiMo-V2.5-Pro |
|---|---|---|
| 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), MiMo-V2.5-Pro: 25.9 (#124)
| Benchmark | GPT-5.6 Terra | MiMo-V2.5-Pro |
|---|---|---|
| NYT Connections (extended) | 78.4% | 34.4% |
| CritPt | 30% | 4% |
| LMArena Hard Prompts | 1467 | 1487 |
| DTBench | 93.3% | 84.5% |
| LMCA | 55% | 29.5% |
| ARC-AGI-2 | 83.9% | — |
| SimpleBench | 48.9% | — |
| Kagi LLM Benchmark | 51.3% | — |
| ARC-AGI-1 | 96.5% | — |
| Chess Puzzles | 54% | — |
| Mystery Game Puzzles | 35% | — |
| Surface Evolver Bench | 83.8% | — |
| Epoch Capabilities Index | 159.31 | — |
Math GPT-5.6 Terra leads
GPT-5.6 Terra: 81.5 (#10), MiMo-V2.5-Pro: 38.6 (#84)
| Benchmark | GPT-5.6 Terra | MiMo-V2.5-Pro |
|---|---|---|
| ProofBench | 74% | 22% |
| LMArena Math | 1463 | 1480 |
| FrontierMath (Tiers 1-3) | 86% | — |
| FrontierMath Tier 4 | 70.7% | — |
| OTIS Mock AIME 2024-2025 | 99.7% | — |
Knowledge GPT-5.6 Terra leads
GPT-5.6 Terra: 61.3 (#27), MiMo-V2.5-Pro: 41.5 (#100)
| Benchmark | GPT-5.6 Terra | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Expert | 1492 | 1501 |
| GPQA Diamond | 93.3% | — |
| SimpleQA Verified | 43.2% | — |
Multimodal Not comparable
GPT-5.6 Terra: 47.5 (#10), MiMo-V2.5-Pro: —
| Benchmark | GPT-5.6 Terra | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Vision | 1271 | — |
| Blueprint-Bench 2 | 30.8% | — |
| Furniture Assembly | 54.2% | — |
| LMArena Document | 1472 | — |
Multilingual Too close to call
GPT-5.6 Terra: 54.5 (#42), MiMo-V2.5-Pro: 55.3 (#33)
| Benchmark | GPT-5.6 Terra | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Non-English | 1439 | 1449 |
| LMArena Chinese | 1514 | 1507 |
| LMArena French | 1476 | 1489 |
| LMArena German | 1462 | 1455 |
| LMArena Japanese | 1446 | 1418 |
| LMArena Korean | 1420 | 1436 |
| LMArena Russian | 1450 | 1450 |
| LMArena Spanish | 1455 | 1470 |
Instruction Following MiMo-V2.5-Pro leads
GPT-5.6 Terra: 76.6 (#38), MiMo-V2.5-Pro: 77.6 (#21)
| Benchmark | GPT-5.6 Terra | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Instruction Following | 1453 | 1477 |
Long Context MiMo-V2.5-Pro leads
GPT-5.6 Terra: 44.4 (#64), MiMo-V2.5-Pro: 45.4 (#38)
| Benchmark | GPT-5.6 Terra | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Longer Query | 1452 | 1482 |
Writing & Preference GPT-5.6 Terra leads
GPT-5.6 Terra: 70.4 (#21), MiMo-V2.5-Pro: 65.5 (#47)
| Benchmark | GPT-5.6 Terra | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Text | 1446 | 1465 |
| LMArena Creative Writing | 1411 | 1440 |
| EQ-Bench Creative Writing | 1855 | 1493 |
| EQ-Bench 4 | 1234 | 1208 |
| LMArena Multi-Turn | 1451 | 1477 |
Frequently asked questions
Is GPT-5.6 Terra better than MiMo-V2.5-Pro?
GPT-5.6 Terra is the stronger model overall, scoring 59.1 to 44.8 on the Noometry Index. MiMo-V2.5-Pro costs 8.3× 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 MiMo-V2.5-Pro?
MiMo-V2.5-Pro is cheaper. It lists at $0.43 per million input tokens and $0.87 per million output tokens; GPT-5.6 Terra lists at $2 and $12.
Is GPT-5.6 Terra or MiMo-V2.5-Pro better for coding?
GPT-5.6 Terra scores higher on coding benchmarks: 57.1 versus 47.0 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Terra does, with 1.05M tokens against 1.05M.
How many benchmarks do GPT-5.6 Terra and MiMo-V2.5-Pro share?
27 benchmarks have published results for both models. GPT-5.6 Terra has 52 scored results on Noometry and MiMo-V2.5-Pro has 27.