GPT-6.1 Sol vs Mimo v2 Omni
Full profiles: GPT-6.1 Sol (OpenAI) and Mimo v2 Omni (Xiaomi): every benchmark, price and rank.
Updated · Data from 9 sources
As of 29 Oct 2026, GPT-6.1 Sol is ahead of Mimo v2 Omni on quality (67.2 vs 58.9 on the BenchLeader Index). GPT-6.1 Sol leads in every category we measure both in.
Key facts (as of 29 Oct 2026)
- GPT-6.1 Sol scores 67.2 on the BenchLeader Index and Mimo v2 Omni scores 58.9 (BenchLeader, 29 Oct 2026).
- GPT-6.1 Sol costs $4.00 per 1M tokens blended (list price, 29 Oct 2026).
- Of the 13 benchmarks both models have, GPT-6.1 Sol leads 13 (BenchLeader, 29 Oct 2026).
GPT-6.1 Sol benchmarks 
Mimo v2 Omni benchmarks Which to pick for…
The pick's numbers come first. Category scores on the BenchLeader scale (50 is the average model, 15 points one standard deviation). High confidence means a lead of half a standard deviation or more; under one point is called either.
| Use case | Pick | Why | Confidence |
|---|---|---|---|
| Coding | GPT-6.1 Sol | 72 vs 62 category score; Arena Coding 1537 vs 1488 | High |
| Agents & tool use | Either | 64 vs 65 category score | Either |
| Maths | GPT-6.1 Sol | 73 vs 61 category score; Arena Maths 1513 vs 1434 | High |
| Reasoning | GPT-6.1 Sol | 73 vs 62 category score; Arena Hard Prompts 1504 vs 1458 | High |
| Long documents | GPT-6.1 Sol | 66 vs 61 category score; AA-LCR v1.1 83.0% vs 73.3% | Medium |
| Images and other media | GPT-6.1 Sol | 66 vs 56 category score; Arena Vision 1288 vs 1228 | High |
| Chat quality (human preference) | GPT-6.1 Sol | 67 vs 61 on Arena votes | Medium |
| High volume on a budget | Neither flagship | A smaller model is usually the better fit: Cheapest good LLMs, Fastest LLMs | – |
Key differences
- GPT-6.1 Sol (max) leads on quality: 67.2 vs 58.9.
- GPT-6.1 Sol (max) is stronger in coding, human preference, knowledge, long context, maths, multimodal, reasoning.
- Mimo v2 Omni is stronger in agents & tools, instruction following.
At a glance
| Spec | GPT-6.1 Sol | Mimo v2 Omni |
|---|---|---|
| Developer | OpenAI | Xiaomi |
| Released | 29 Sept 2026 | 27 Mar 2026 |
| Context window (tokens) | 1,050,000 | 256,000 |
| Compared at | GPT-6.1 Sol (max) | Mimo v2 Omni |
| Open weights | No | No |
| Price in / out / cached input per 1M | $2.00 / $10.00 / $0.100 | – |
Category by category
Category scores on the BenchLeader scale: 50 is the average evaluated model, 15 points is one standard deviation. A gap under one point is called level.
| Category | GPT-6.1 Sol (max) | Mimo v2 Omni | Leader |
|---|---|---|---|
| Reasoning | 73.2 | 61.9 | GPT-6.1 Sol |
| Coding | 71.6 | 62.1 | GPT-6.1 Sol |
| Agents & tools | 64.4 | 64.8 | level |
| Maths | 72.8 | 61.0 | GPT-6.1 Sol |
| Knowledge | 73.0 | – | not comparable |
| Instruction following | – | 66.5 | not comparable |
| Human preference | 67.1 | 61.1 | GPT-6.1 Sol |
| Multimodal | 65.9 | 55.9 | GPT-6.1 Sol |
| Long context | 66.4 | 61.4 | GPT-6.1 Sol |
Where sources disagree
Across the 13 benchmarks both have, GPT-6.1 Sol leads 13, Mimo v2 Omni leads 0. Every overall index that rates both puts GPT-6.1 Sol ahead.
| Overall index | GPT-6.1 Sol (max) | Mimo v2 Omni | Leader |
|---|---|---|---|
| BenchLeader Index | 67.2 | 58.9 | GPT-6.1 Sol |
| Arena Text | 1480 | 1431 | GPT-6.1 Sol |
Each publisher runs its own tasks, harnesses and effort settings, so their overall verdicts can differ; BenchLeader reconciles them on one scale. † measured at another effort setting of the same model (the publisher did not test the one compared here).
Price, speed and latency
List prices blended at three input tokens per output token. Per-task prices use the token counts on the methodology page; suite cost is what Artificial Analysis paid to run its whole Intelligence Index.
| Measure | GPT-6.1 Sol (max) | Mimo v2 Omni |
|---|---|---|
| Blended price per 1M tokens | $4.00 | – |
| Input / output per 1M | $2.00 / $10.00 | – / – |
| Cached input per 1M | $0.100 | – |
| Price per task: chat reply | $0.0038 | – |
| Price per task: summarize a 30-page report | $0.030 | – |
| Price per task: code edit | $0.027 | – |
| Price per task: agentic coding session | $0.160 | – |
| Price per task: structured extraction | $0.0060 | – |
| Cost to run a full benchmark suite | $1.27 | – |
| Output speed | 79 tok/s | – |
| Time to first answer | 249.5 s | – |
| Time to a complete answer | 255.8 s | – |
Each model is compared at its best-ranked reasoning-effort setting. GPT-6.1 Sol (max) scores 67.2 and takes 249 s to its first answer token; GPT-6.1 Sol (xhigh) scores 64.3 in 97 s, GPT-6.1 Sol (high) scores 64.0 in 28 s. Mimo v2 Omni scores 58.9. Higher effort means more thinking before the answer: better scores, longer waits and more tokens billed. How reasoning effort changes scores and cost.
Every benchmark
118 benchmarks, grouped by category; each name links to the board and its leaders. † measured at another effort setting of the same model, shown for reference and not counted as a win.
| Metric | Publisher | GPT-6.1 Sol (max) | Mimo v2 Omni |
|---|---|---|---|
| BenchLeader Index | BenchLeader | 67.2 | 58.9 |
| Agents & tools score | BenchLeader | 64.4 | 64.8 |
| Coding score | BenchLeader | 71.6 | 62.1 |
| Human preference score | BenchLeader | 67.1 | 61.1 |
| Knowledge score | BenchLeader | 73.0 | – |
| Long context score | BenchLeader | 66.4 | 61.4 |
| Maths score | BenchLeader | 72.8 | 61.0 |
| Multimodal score | BenchLeader | 65.9 | 55.9 |
| Reasoning score | BenchLeader | 73.2 | 61.9 |
| Instruction following score | BenchLeader | – | 66.5 |
| Blended price $/M | BenchLeader | $4.00 | – |
| Output speed | BenchLeader | 79 tok/s | – |
| Time to first answer | BenchLeader | 249.5 s | – |
| Context window | BenchLeader | 1,050,000 tokens | 256,000 tokens |
| Reasoning | |||
| GPQA Diamond | Epoch AI Benchmarking Hub | 95.4% | – |
| Arena Hard Prompts | Arena (LMArena) | 1504 | 1458 |
| LiveBench 2.0 Reasoning | LiveBench | 92.6%† | – |
| LiveBench 1.0 Reasoning | LiveBench | 92.6% | – |
| GPQA Diamond (AA) | Artificial Analysis | – | 85.5% |
| Humanity's Last Exam (AA) | Artificial Analysis | 52.9% | 22.6% |
| HLE-Diamond (AA) | Artificial Analysis | 58.8% | – |
| ARC-AGI-1 | ARC Prize | 96.5% | – |
| ARC-AGI-2 | ARC Prize | 94.2% | – |
| ARC-AGI-3 | ARC Prize | 96.2% | – |
| CritPt | Artificial Analysis | 31.7% | 0.9% |
| CritPt v2 hard subset (AA) | Artificial Analysis | 68.5% | – |
| MysteryMechanism | Vals AI | 46.4% | – |
| Chess Puzzles | Epoch AI Benchmarking Hub | 68.0% | – |
| Mystery Game Puzzles | Epoch AI Benchmarking Hub | 80.0% | – |
| LMCA | Epoch AI Benchmarking Hub | 62.8% | – |
| DTBench | Epoch AI Benchmarking Hub | 96.5% | – |
| Coding | |||
| SciCode | SciCode | 54.2% | – |
| FrontierCode | Cognition | 50.2%† | – |
| Arena Coding | Arena (LMArena) | 1537 | 1488 |
| Arena WebDev | Arena (LMArena) | 1752 | – |
| LiveBench 2.0 Coding | LiveBench | 96.0%† | – |
| LiveBench 1.0 Coding | LiveBench | 80.4% | – |
| IOI | Vals AI | 96.9% | – |
| Code Migration | Vals AI | 65.1% | – |
Show all 118 benchmarks
| Metric | Publisher | GPT-6.1 Sol (max) | Mimo v2 Omni |
|---|---|---|---|
| Coding | |||
| Code Migration v1.1 | Vals AI | 77.3% | – |
| ProgramBench | Vals AI | 3.0% | – |
| SWE-bench Multimodal | Vals AI | 43.5% | – |
| Vibe Code Bench v1.1 | Vals AI | 88.9% | – |
| ALE-Bench | Epoch AI Benchmarking Hub | 2743.8 | – |
| Terminal-Bench 4.0 (AA) | Artificial Analysis | 56.1% | – |
| Startrise Frontend | Startrise | 78.6%† | – |
| FrontierCode 1.1 (AA) | Artificial Analysis | 49.1% | – |
| FrontierCode 2.0 (AA) | Artificial Analysis | 40.6% | – |
| Agents & tools | |||
| Terminal-Bench | Terminal-Bench | 58.2% | – |
| APEX-Agents | Mercor | 60.0% | – |
| Arena Agent | Arena (LMArena) | 13.6 | – |
| LiveBench Long Agentic | LiveBench | 68.7%† | – |
| LiveBench Browser | LiveBench | 93.3%† | – |
| LiveBench Agentic Coding | LiveBench | 54.5% | – |
| Terminal-Bench Hard | Artificial Analysis | – | 35.6% |
| τ²-Bench Telecom (AA) | Artificial Analysis | – | 88.0% |
| HiL-Bench | Scale AI SEAL | 62.3%† | – |
| GDPval-AA v2.1, normalised score | Artificial Analysis | 54.8% | – |
| GDPval-AA v2.1 | Artificial Analysis | 1608 | – |
| Analyst Agent (AA) | Artificial Analysis | 50.0% | – |
| CyberBench | Vals AI | 39.3% | – |
| Finance Agent v2 | Vals AI | 52.0% | – |
| Finance Agent v2.1 | Vals AI | 61.8% | – |
| Harvey's Legal Agent Benchmark v1.1 | Vals AI | 6.3% | – |
| Harvey's Legal Agent Benchmark v1 | Vals AI | 5.4% | – |
| Legal Research Bench | Vals AI | 38.5% | – |
| Robo-bench | Vals AI | 28.3% | – |
| SREBench | Vals AI | 50.8% | – |
| Tax Agent Bench | Vals AI | 21.8% | – |
| Terminal-Bench 4.0 (Vals) | Vals AI | 55.0% | – |
| Terminal-Bench Science | Vals AI | 52.9% | – |
| Arena Search | Arena (LMArena) | 1250 | – |
| AutomationBench | Artificial Analysis | 64.9% | – |
| GDP.pdf (AA) | Artificial Analysis | 31.0% | – |
| Harvey LAB-AA v1.1 | Harvey | 6.9% | – |
| AA-Briefcase v1.1 | Artificial Analysis | 1555 | – |
| Terminal-Bench-Science 0.1 (AA) | Artificial Analysis | 58.1% | – |
| FACTS Search | 88.2%† | – | |
| Maths | |||
| FrontierMath Tiers 1–3 | Epoch AI Benchmarking Hub | 93.7% | – |
| FrontierMath Tier 4 | Epoch AI Benchmarking Hub | 100.0% | – |
| OTIS Mock AIME | Epoch AI Benchmarking Hub | 100.0% | – |
| ProofBench | Vals AI | 99.0%† | – |
| LiveBench Mathematics | LiveBench | 96.8% | – |
| Arena Maths | Arena (LMArena) | 1513 | 1434 |
| Knowledge | |||
| SimpleQA Verified | Epoch AI Benchmarking Hub | 73.9% | – |
| LiveBench 2.0 Data Analysis | LiveBench | 82.7%† | – |
| LiveBench Research | LiveBench | 83.5%† | – |
| LiveBench 1.0 Data Analysis | LiveBench | 82.7% | – |
| AA-Omniscience v1.1 | Artificial Analysis | 42.0 | – |
| PRBench Finance | Scale AI SEAL | 46.2%† | – |
| PRBench Legal | Scale AI SEAL | 47.9%† | – |
| BioMysteryBench | Vals AI | 79.6% | – |
| ClaimBench | Vals AI | 100.0% | – |
| Excel Modeling Benchmark | Vals AI | 70.8% | – |
| Excel Modeling Benchmark v1.1 | Vals AI | 71.1% | – |
| MedCode | Vals AI | 48.8% | – |
| MedScribe | Vals AI | 86.5% | – |
| Public Benefits Bench v1.1 | Vals AI | 59.3% | – |
| EBR-bench | Epoch AI Benchmarking Hub | 54.3% | – |
| GDP.pdf | Epoch AI Benchmarking Hub | 35.0% | – |
| AA-Omniscience v1.1: accuracy | Artificial Analysis | 61.8% | – |
| AA-Omniscience v1.1: non-hallucination | Artificial Analysis | 48.1% | – |
| HealthBench Professional (AA) | OpenAI | 65.1% | – |
| Instruction following | |||
| LiveBench Language | LiveBench | 90.1% | – |
| LiveBench Instruction Following | LiveBench | 74.2% | – |
| IFBench | Artificial Analysis | – | 67.3% |
| MultiChallenge | Scale AI SEAL | 82.0%† | – |
| Arena Instruction Following | Arena (LMArena) | 1485 | 1428 |
| Human preference | |||
| Arena Text | Arena (LMArena) | 1480 | 1431 |
| EQ-Bench 4 | EQ-Bench | 1272† | – |
| Arena Creative Writing | Arena (LMArena) | 1453 | 1395 |
| Arena Multi-turn | Arena (LMArena) | 1483 | 1452 |
| Arena Longer Queries | Arena (LMArena) | 1489 | 1450 |
| Multimodal | |||
| Arena Vision | Arena (LMArena) | 1288 | 1228 |
| LiveBench Design | LiveBench | 82.1%† | – |
| MMMU-Pro | Artificial Analysis | 86.0% | 73.9% |
| SAGE | Vals AI | 90.5% | – |
| Code2Video-AA | Artificial Analysis | 1369 | – |
| AA-3D | Artificial Analysis | 1119 | – |
| Long context | |||
| AA-LCR v1.1 | Artificial Analysis | 83.0% | 73.3% |
| MLCR | Artificial Analysis | 38.3% | – |
| Composite | |||
| Epoch Capabilities Index | Epoch AI Benchmarking Hub | 168.4† | – |
| LiveBench 2.0 | LiveBench | 84.1%† | – |
| LiveBench 1.0 | LiveBench | 81.6% | – |
| AA Intelligence Index v5.0 | Artificial Analysis | 54.5 | – |
| AA Intelligence Index v4.3.2 | Artificial Analysis | 51.8 | 25.1 |
| AA Progress Index v1 | Artificial Analysis | 348.5 | 269 |
| Vals Index | Vals AI | 63.9 | – |
| Vals Finance Index | Vals AI | 55.8% | – |
| Vals RSI Index | Vals AI | 26.6% | – |
| Vals Science Index | Vals AI | 59.6% | – |
| Vals Router Index | Vals AI | 63.9%† | – |
Data as of 2026-10-29. Best configuration of each model; every score links to its source on the model pages.
What changed
- GPT-6.1 Sol (max): 5 new results (AA Intelligence Index v5.0, AA Progress Index v1, HLE-Diamond (AA)…); 3 score revisions; cost per task moved from $0.72/M to $1.27/M (76% up).
- Mimo v2 Omni: 1 new result (AA Progress Index v1).
- GPT-6.1 Sol (max): 10 new results (ClaimBench, Code Migration v1.1, Excel Modeling Benchmark v1.1…).
- GPT-6.1 Sol (max): BenchLeader Index 67.9 → 67.4.
- GPT-6.1 Sol (max): 5 score revisions.
- GPT-6.1 Sol (max): 2 score revisions.
- GPT-6.1 Sol (max): 2 new results (LMCA, DTBench).
- Mimo v2 Omni: BenchLeader Index 57.5 → 58.7.
- Mimo v2 Omni: 7 score revisions.
- GPT-6.1 Sol (max): 2 score revisions.
Related comparisons
- GPT-6.1 Sol vs Claude Opus 5.5
- GPT-6.1 Sol vs GPT-6 Astra
- GPT-6.1 Sol vs Claude Fable 5.1
- Mimo v2 Omni vs Claude Opus 5.5
- Mimo v2 Omni vs GPT-6 Astra
- Mimo v2 Omni vs Claude Fable 5.1
- GPT-6.1 Sol vs Mimo v2 Pro
- GPT-6.1 Sol vs Gemini 4 Argon
Rankings: Best LLM overall · Best LLM for coding · Best LLM for agents · Best cheap LLMs
GPT-6.1 Sol vs Mimo v2 Omni: questions
- Is GPT-6.1 Sol better than Mimo v2 Omni?
- GPT-6.1 Sol (max) leads on quality: 67.2 vs 58.9. The BenchLeader Index combines every independent quality benchmark; GPT-6.1 Sol (max) is ahead overall as of 29 Oct 2026, but check the category scores for your use.
- Is GPT-6.1 Sol or Mimo v2 Omni better for coding?
- GPT-6.1 Sol scores higher in coding (72 vs 62 on the category index, where 50 is average). On Arena Coding, the top shared coding benchmark, it is 1537 for GPT-6.1 Sol against 1488 for Mimo v2 Omni.
- Is GPT-6.1 Sol or Mimo v2 Omni better for agents and tool use?
- They are level in agentic tasks (64 vs 65 on the category index).
- Which is better for long documents?
- GPT-6.1 Sol scores higher on long-context benchmarks (66 vs 61). GPT-6.1 Sol accepts more context: 1,050,000 against 256,000 tokens for Mimo v2 Omni.
- Do independent benchmarks agree on GPT-6.1 Sol vs Mimo v2 Omni?
- Across the 13 benchmarks both have, GPT-6.1 Sol leads 13, Mimo v2 Omni leads 0. Every overall index that rates both puts GPT-6.1 Sol ahead.
More questions (1)
- When was GPT-6.1 Sol released?
- GPT-6.1 Sol was released on 29 Sept 2026.