MiniMax M2.7 vs GPT-5.5
At a Glance
| Compare |  MiniMax M2.7MiniMax |  GPT-5.5OpenAI |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | UnrankedNot in the 46-model eligible cohort | #7 of 4683.9 score · 3/3 sources · complete |
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #38 of 44$0.341 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | UnrankedNot in the 38-model eligible cohort | #18 of 3853.4 score · 3/3 sources · complete |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.25Deepinfra ↗ · Sep 3, 2026 | $5.00Openai ↗ · Sep 3, 2026 |
| Output priceFrom · USD / 1M tokens | $1.00Deepinfra ↗ · Sep 3, 2026 | $30.00Openai ↗ · Sep 3, 2026 |
| Context windowMaximum documented tokens | 205K | 1,050K |
| Model facts checked | Aug 28, 2026View model evidence → | Sep 3, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
| Benchmark | MiniMax-M2.7 | GPT-5.5 |
|---|---|---|
| LMArena Agent Arenaagent-2026-09-15-d25aabda0010 · outcome_score · leader | -14.0284% of row best · score · Minimax M2.7; 95% CI [-15.09137111, -12.95249504]; sessions 37764; observations 858163; rank 44 | 2.67100% of row best · score · GPT 5.5; 95% CI [1.85777151, 3.48040109]; sessions 81254; observations 2107641; rank 20 |
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader | 1,404.6196% of row best · rating · minimax-m2.7; 95% CI [1401.03588542, 1408.19095698]; votes 69169; rank 138 | 1,465.59100% of row best · rating · gpt-5.5; 95% CI [1461.82849712, 1469.35015015]; votes 66317; rank 31 |
| Overall ResultCounted from the protocol-matched rows above | 0 benchmark winsNo overall winner | 2 benchmark winsNo overall winner |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Side-by-Side Facts
| Field | MiniMax-M2.7 | GPT-5.5 |
|---|---|---|
| Developer | MiniMax | OpenAI |
| Family | Minimax M2 7 | Gpt 5 5 |
| Model | MiniMax-M2.7 | GPT-5.5 |
| Version | MiniMax-M2.7 | GPT-5.5 |
| Lifecycle | active | active |
| Released | 2026-03-18 | 2026-04-23 |
| Knowledge cutoff | Unknown | 2025-12-01 |
| Input modalities | Text | Text, Image |
| Output modalities | Text | Text |
| Context window | 205K | 1,050K |
| Total parameters | 228.7B | Unknown |
| Active parameters | Unknown | Unknown |
| License | other | Unknown |
| Open weights | Yes | No |
| API available | Yes | Yes |
| Self-hostable | Yes | No |
| Provider access | Deepinfra (Standard), Fireworks Ai (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) | Openai (Standard), Openrouter (Standard) |
| Capabilities | chat, generation, tools | chat, generation, reasoning, structured_outputs, tools |
MiniMax M2.7 Capabilities
GPT-5.5 Capabilities
Related Comparisons
All text comparisons →| A | Pair | B | Context |
|---|---|---|---|
MiniMax-M2.7MiniMax | vs | GPT-6 AstraOpenAI | cross-developer peerstext |
MiniMax-M2.7MiniMax | vs | Claude Fable 5.1Anthropic | cross-developer peerstext |
MiniMax-M2.7MiniMax | vs |  Gemini 3.1 ProGoogle DeepMind | cross-developer peerstext |
MiniMax-M2.7MiniMax | vs |  DeepSeek-V4.1-FlashDeepSeek | cross-developer peerstext |
MiniMax-M2.7MiniMax | vs | Grok 4.6xAI | cross-developer peerstext |
MiniMax-M2.7MiniMax | vs |  Qwen3.8-MaxQwen | cross-developer peerstext |
MiniMax-M2.7MiniMax | vs |  Kimi-K3Moonshot AI | cross-developer peerstext |
MiniMax-M2.7MiniMax | vs | MiniMax-M3MiniMax | family variantstext |
MiniMax-M2.7MiniMax | vs |  GLM-5.3Z.ai | cross-developer peerstext |
MiniMax-M2.7MiniMax | vs |  Hy4 previewTencent | cross-developer peerstext |
MiniMax-M2.7MiniMax | vs | Seed 2.1 ProByteDance Seed | cross-developer peerstext |
MiniMax-M2.7MiniMax | vs |  Mistral Large 3Mistral AI | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
MiniMax M2.7 vs GPT-5.5 FAQs
Is MiniMax M2.7 or GPT-5.5 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both MiniMax M2.7 and GPT-5.5, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, MiniMax M2.7 or GPT-5.5?+
MiniMax M2.7 is $0.25 and GPT-5.5 is $5.00 per million tokens, so MiniMax M2.7 is cheaper on this metric. MiniMax M2.7 is $1.00 and GPT-5.5 is $30.00 per million tokens, so MiniMax M2.7 is cheaper on this metric.
Which has a larger context window, MiniMax M2.7 or GPT-5.5?+
GPT-5.5 has the larger sourced context window. MiniMax M2.7 supports 205K and GPT-5.5 supports 1,050K.
Which performs better in benchmarks, MiniMax M2.7 or GPT-5.5?+
There is no overall benchmark winner: An overall winner requires at least two decisive benchmarks from at least two original publishers.
Can MiniMax M2.7 or GPT-5.5 be self-hosted?+
MiniMax M2.7 is the only model in this pair currently marked as self-hostable. MiniMax M2.7 is open weight; GPT-5.5 is not marked open weight.
Can MiniMax M2.7 and GPT-5.5 understand images?+
MiniMax M2.7 is not documented with image input; GPT-5.5 is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, MiniMax M2.7 or GPT-5.5?+
Neither has a larger sourced maximum output. MiniMax M2.7 is — and GPT-5.5 is 128K.
Do MiniMax M2.7 and GPT-5.5 support reasoning and tool use?+
MiniMax M2.7: tool calling. GPT-5.5: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, MiniMax M2.7 or GPT-5.5?+
MiniMax M2.7 has 5 sourced provider routes; GPT-5.5 has 2, so MiniMax M2.7 has broader tracked availability.
Which offers better value, MiniMax M2.7 or GPT-5.5?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.