GPT-5.5 vs MiniMax M2.7
GPT-5.5 wins on Overall, Coding, Multimodal. Every numeric field is compared below, with a worked monthly-cost example and a pick rule for each use case.
Verdict at a glance
Choose GPT-5.5 if you…
- Balanced with no weak spot
- Richest ecosystem
- Mature plugin/function calling
- Strong creative writing
- Best for: General tasks, Creative writing, Coding
Choose MiniMax M2.7 if you…
- Light and fast
- Open source
- Low price
- Best for: Light tasks, Open source, Low cost
Head-to-head aggregate scores
Scores are 0–100, aggregated from public benchmark information and independently weighted across three leaderboards. Rank is out of 22 tracked models.
Aggregated from public sources and independently weighted; methodology on the Terms page. Scores within 3 points are treated as statistically tied.
Specs & pricing — every field side by side
List API prices in USD per 1M tokens. The highlighted cell is the stronger value/capability on that row.
| Dimension | GPT-5.5 | MiniMax M2.7 | Verdict |
|---|---|---|---|
| Vendor | OpenAI (US) | MiniMax (CN) | Different vendors |
| Released | 2026.04 | 2026.05 | MiniMax M2.7 is newer |
| Overall (rank) | 81 · #5 | 64 · #19 | GPT-5.5 +17 |
| Coding | 75 · #8 | 59 · #21 | GPT-5.5 +16 |
| Multimodal | 85 · #8 | 62 · #22 | GPT-5.5 +23 |
| Context window | 1.05M | 205K | GPT-5.5 larger |
| Max output | 128K | 32K | MiniMax M2.7 longer |
| Effective-context | 90 | 85 | GPT-5.5 more reliable |
| Input $/1M | $5 | $0.27 | MiniMax M2.7 cheaper |
| Output $/1M | $30 | $1.08 | MiniMax M2.7 cheaper |
| Cache discount | 50% off | none | GPT-5.5 deeper |
| Speed | ~50 tok/s | ~70 tok/s | MiniMax M2.7 faster |
| TTFT | 0.8s | 0.4s | MiniMax M2.7 snappier |
| Function calling | 95 | 72 | GPT-5.5 ahead |
| Refusal rate | ~15% | ~6% | MiniMax M2.7 less restrictive |
| English | 94 | 65 | GPT-5.5 |
| Chinese | 76 | 82 | MiniMax M2.7 |
| Modalities | text, image | text | different coverage |
| Open weights | No | Yes | MiniMax M2.7 is open |
| Fine-tuning | Yes | Yes | |
| Free tier | ChatGPT free tier available (rate-limited); Plus $20/month | Hailuo AI free | |
| SOC2 / no-train | yes / yes | no / yes | |
| Private deployment | No | Yes | MiniMax M2.7 |
Fields drawn from vendor public documentation and the Modelspectra 22-model dataset; speed varies with network, concurrency and prompt length. Verify current pricing before purchase.
Dimension-by-dimension analysis
Reasoning & overall intelligence
GPT-5.5 leads the overall aggregate by 17 points (81 vs 64). That is a meaningful, not marginal, edge on hard, multi-step reasoning, while MiniMax M2.7 remains a strong generalist that is not out of its depth on routine work.
Agentic coding
This is a clear gap: GPT-5.5 scores 75 against 59. On multi-file edits, SWE-style tickets and long-horizon agent loops GPT-5.5 needs fewer correction turns; MiniMax M2.7 is still competent for scripts and assisted completion but trails as task complexity rises.
Multimodal
GPT-5.5 leads multimodal 85 vs 62. A concrete modality difference: GPT-5.5 additionally handles image. Neither emits native video, so the comparison is about parsing images and documents, not generation.
Speed & latency
MiniMax M2.7 is faster in interactive use: ~70 tok/s with 0.4s TTFT versus ~50 tok/s with 0.8s TTFT (about 1.4× the throughput). For conversational UIs where perceived responsiveness drives retention, that edge is a real product factor even when raw reasoning is lower.
Context: window vs usable recall
Nominal windows are 1.05M for GPT-5.5 and 205K for MiniMax M2.7. Effective-context scores point the same way as window size — GPT-5.5 is ahead on usable recall (90 vs 85), so prefer it for long-document work where details cannot be missed.
Price & total cost
MiniMax M2.7 is the cheaper API at $0.27/$1.08 versus GPT-5.5 at $5/$30 per 1M input/output tokens — list input is about 18.5× lower. Cache discounts (GPT-5.5 50%) shift the effective bill, worked out below.
Chinese vs English
English: GPT-5.5 94 vs MiniMax M2.7 65. Chinese: 76 vs 82. For Chinese-language production, MiniMax M2.7 is the stronger pick. Note that non-Chinese models generally require overseas network access for their APIs.
Tool use & ecosystem
Function-calling score: GPT-5.5 95 vs MiniMax M2.7 72, so GPT-5.5 has the edge on structured tool use. Fine-tuning is available from GPT-5.5 and MiniMax M2.7. Factor in existing SDK/plugin familiarity — switching cost often outweighs a few-point tool-use gap.
Cost worked example — same workload, real token math
Assume a production workload of 100M input + 30M output tokens per month, with 90% of input tokens served from cache. Figures use public list prices.
| Scenario · per month | GPT-5.5 | MiniMax M2.7 | Gap |
|---|---|---|---|
| List priceno cache applied | $1,400100M in × $5 + 30M out × $30 | $59100M in × $0.27 + 30M out × $1.08 | 23.73×gap |
| With caching90% of inputs cache-hit | $1,17590M cached in × $2.5 + 10M in × $5 + 30M out × $30 | $59no published cache discount | 19.92×gap |
Illustrative model; your input/output mix and cache-hit ratio change the result. Prices are list rates before any enterprise agreement.