Model comparison
GPT-5.5 vs MiniMax M3
OpenAI against MiniMax, compared on context, price, and verified benchmark results.
Catalog record checked September 5, 2026Individual provider fields may changeEvidence confidence: High — see receipts below
OpenAI
GPT-5.5
Frontier
MiniMax
MiniMax M3
Frontier · Open weights
| Specification | GPT-5.5 | MiniMax M3 |
|---|---|---|
| Provider | OpenAI | MiniMax |
| Tier | Frontier | Frontier |
| Context window | Winner: 1.05M | 1M |
| Max output | 128K | Not verifiedUnverified |
| Input / 1M tokens | $5 | Winner: $0.30 |
| Output / 1M tokens | $30 | Winner: $1.20 |
| Weights | Closed | Open |
| Parameters | Not disclosedUnverified | 428B total / 23B active (MoE) |
| Reasoning levels | none, low, medium, high, xhigh, max | low, high, max |
| Modalities | text, image | text, image, video |
| API model id | gpt-5.5 | minimax-m3 |
| Released | April 23, 2026 | June 1, 2026 |
| Artificial Analysis Intelligence Index [xhigh] (2026-08-14) | 58 | Not verifiedUnverified |
| Artificial Analysis Intelligence Index (2026-08-14) | Not verifiedUnverified | 51 |
| SWE-bench Verified (2026-09-01) | 82.6 | Not verifiedUnverified |
| Terminal-Bench 2.1 (2026-07-27) | Winner: 83.4 | 66 |
| GPQA Diamond (2026-07-27) | 93.5 | Not verifiedUnverified |
| Humanity's Last Exam (2026-04-23) | 52.2 | Not verifiedUnverified |
Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Bold indicates the better figure where one is objectively better. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.
Benchmark receipts
- 2026-09-01: vals.ai SWE-bench Verified leaderboard (independent, mini-swe-agent bash-only); official SWE-bench not publishedReal GitHub issue resolution: does the model's patch pass the hidden tests. Comparability: comparable with caveat — Post-audit vendor claims and pre-audit scores sit on different task trust levels; scaffolding (agent harness, compute budget) also dominates results. Never aggregate across scaffolds.
- 2026-08-14: Artificial AnalysisComposite index blending reasoning, knowledge, and coding evals into one 0–100 score. Comparability: directly comparable — AA occasionally rebaselines the index scale between snapshots — a score captured on one date is only comparable to same-snapshot scores (check measuredAt).
- 2026-07-27: Kimi K3 model card (vendor-reported, xhigh); announcement charts show 93.6 — minor conflict preservedAgentic terminal work: multi-step tasks executed in a sandboxed shell environment. Comparability: comparable with caveat — Scores across Terminal-Bench major versions (v1 vs v2) are NOT comparable; harness configuration (container, time limits) also shifts results.
- 2026-06-01: MiniMax blog (vendor, Terminus 2 scaffolding, 2h timeout)Agentic terminal work: multi-step tasks executed in a sandboxed shell environment. Comparability: comparable with caveat — Scores across Terminal-Bench major versions (v1 vs v2) are NOT comparable; harness configuration (container, time limits) also shifts results.
- 2026-04-23: OpenAI — Introducing GPT-5.5 (vendor, with tools; 41.4 no tools)Frontier-knowledge ceiling: extremely hard multi-domain questions written to be near-impossible without deep expertise. Comparability: comparable with caveat — Subset/tool configurations (text-only vs with-tools) differ between vendors and materially change scores; must match configuration to compare.
Pricing tiers: GPT-5.5: Standard $5/$30 per MTok; >272K input billed 2x in / 1.5x out. Cached input $0.50/MTok; batch $2.50/$15. · MiniMax M3: $0.30/$1.20 per MTok; open (community license). 428B/23B active MoE, native multimodal. Rate is the ≤512K-prompt tier; higher above.
GPT-5.5
GPT-5.5 is OpenAI's previous flagship — strong on Terminal-Bench 2.0 (82.7%) and 1M-token context.
Best for
- Reasoning
- Agentic coding
- Long-context work
Watch out
Superseded by GPT-5.6 Sol on quality; still a capable, widely integrated model.
MiniMax M3
MiniMax M3 is an open-weight (community license) 428B MoE with native multimodal and 1M context.
Best for
- Open-weight deployments
- Multimodal
- Long-context
Watch out
Community license, not fully open; verify terms.
When the cheaper one wins
MiniMax M3 is cheaper on output at $1.20 per million tokens against $30 for GPT-5.5 — about 25×. Use the cheaper tier for classification, extraction, summarisation, and any task where the expensive model’s extra score does not change the accepted output. The expensive one only pays if your hardest task actually fails on the cheap tier. These are standard-tier API rates, excluding batch and cache discounts.
Run the model pickerEvidence confidence: High
How strong and complete the evidence behind this comparison is — not a prediction of which model is better.
- Pricing verified on both sides — Input and output rates are verified for both models.
- 3/5 core specs verified on both sides — Not published for at least one side: max output, parameter count.
- 2 shared named benchmarks with differing scores — Measured on: Artificial Analysis Intelligence Index, Terminal-Bench 2.1.
- Verified within the last 90 days — Newest catalog check was 6 days ago.
- Both models carry source citations — Each side has at least two catalog sources on record.
Source receipts
Each catalog figure was checked against the provider or an independent second source on the date shown.
- GPT-5.5: OpenAI — Introducing GPT-5.5 (accessed 2026-08-29)
- GPT-5.5: OpenAI API pricing (gpt-5.5 $5/$30) (accessed 2026-08-29)
- MiniMax M3: MiniMax — M3 (accessed 2026-08-29)
- MiniMax M3: MiniMax M3 release (accessed 2026-08-29)
Related comparisons
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- Claude Fable 5 vs MiniMax M3
- Claude Mythos 5.1 vs GPT-5.5
- Claude Mythos 5.1 vs MiniMax M3
Diving deeper on one model? GPT-5.5 · MiniMax M3
Common questions
GPT-5.5 vs MiniMax M3
Answered from the verified figures on this page rather than general guidance.
Is GPT-5.5 or MiniMax M3 cheaper for input?
MiniMax M3 is cheaper at $0.30 per million input tokens, against $5 for GPT-5.5 — roughly 17× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; GPT-5.5 has tiered pricing: Standard $5/$30 per MTok; >272K input billed 2x in / 1.5x out. Cached input $0.50/MTok; batch $2.50/$15. MiniMax M3 has tiered pricing: $0.30/$1.20 per MTok; open (community license). 428B/23B active MoE, native multimodal. Rate is the ≤512K-prompt tier; higher above.
Is GPT-5.5 or MiniMax M3 cheaper for output?
MiniMax M3 is cheaper at $1.20 per million output tokens, against $30 for GPT-5.5 — roughly 25× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; GPT-5.5 has tiered pricing: Standard $5/$30 per MTok; >272K input billed 2x in / 1.5x out. Cached input $0.50/MTok; batch $2.50/$15. MiniMax M3 has tiered pricing: $0.30/$1.20 per MTok; open (community license). 428B/23B active MoE, native multimodal. Rate is the ≤512K-prompt tier; higher above.
Which has the larger context window, GPT-5.5 or MiniMax M3?
GPT-5.5 accepts 1.05M tokens against 1M for MiniMax M3. This only matters if you routinely send very long documents or large codebases.
Do GPT-5.5 and MiniMax M3 support the same reasoning levels?
GPT-5.5 exposes none, low, medium, high, xhigh, max, while MiniMax M3 exposes low, high, max.
Should I use GPT-5.5 or MiniMax M3?
Both sit in the frontier tier, so the choice usually comes down to price and context rather than capability. GPT-5.5 suits reasoning; MiniMax M3 suits open-weight deployments.
Can I self-host GPT-5.5 or MiniMax M3?
MiniMax M3 publishes open weights, but self-hosting and third-party provider access depend on the licence, hardware, serving support, and availability. GPT-5.5 is a closed model whose supported access paths are controlled by its provider.
Next step
Choosing between them
The verified figures that separate this pair, computed from the catalog rather than restated boilerplate.
- Input price: MiniMax M3 costs $0.30 per 1M tokens versus $5 for GPT-5.5 — a 16.7x difference at the headline tier.
- Context: GPT-5.5 takes 1.05M against 1M for MiniMax M3 — only decisive if your prompts approach the smaller window.
- Measured capability: GPT-5.5 leads Artificial Analysis Intelligence Index 58 to 51 (measured 2026-08-14).
- Deployment: MiniMax M3 publishes weights you can self-host; the other is API-only.
Choosing a harness rather than a model — Cursor, Copilot, Claude Code, Windsurf, or Lovable? Compare agentic harnesses · Latest releases.