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Model comparison

GPT-5.5 vs MiniMax M3

OpenAI against MiniMax, compared on context, price, and attributed benchmark results.

Recorded profile verification October 2, 2026Individual provider fields may changeEvidence confidence: Medium — see receipts below

OpenAI

GPT-5.5

Frontier

vs

MiniMax

MiniMax M3

Frontier · Open weights

AI model capability comparison
SpecificationGPT-5.5MiniMax M3
ProviderOpenAIMiniMax
TierFrontierFrontier
Context windowWinner: 1.05M1M
Max output128KNot verifiedUnverified
Input / 1M tokens$5Winner: $0.30
Output / 1M tokens$30Winner: $1.20
WeightsClosedOpen
ParametersNot verifiedUnverified428B total / 23B active (MoE)
Reasoning levelsnone, low, medium, high, xhighNot verifiedUnverified
Modalitiestext, imagetext, image, video
LicenseNot verifiedUnverifiedMiniMax Community License
API model idgpt-5.5MiniMax-M3
ReleasedApril 23, 2026June 1, 2026
Terminal-Bench 2.1 · source date GPT-5.5: 2026-07-27, MiniMax M3: 2026-06-01 (comparability not verified)83.466
GPQA Diamond · source date GPT-5.5: 2026-07-2793.5Not verifiedUnverified
Humanity's Last Exam · source date GPT-5.5: 2026-04-2352.2Not verifiedUnverified
Artificial Analysis Intelligence Index · source date MiniMax M3: 2026-10-01Not verifiedUnverified29

Dates beside benchmark scores refer to the linked source’s publication or capture date unless that source explicitly states when it measured the result. A shared URL alone does not establish comparable runs; unverified setups are marked. Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.

Benchmark receipts

Where each score comes from, and how far it can be compared across models.

  • Source date2026-10-01
    Artificial Analysis (MiniMax-M3)

    Composite index blending reasoning, knowledge, and coding evals into one 0–100 score.

    Comparable with caveatOnly same-version scores are comparable. v4.3.2 was rebaselined (the top score fell from 66 on v4.1.1 to ~58), so v4.1.1 figures must not be compared with v4.3.2 figures — check the cited source and its date.

  • Source date2026-07-27
    Kimi K3 model card (vendor-reported, xhigh); announcement charts show 93.6 — minor conflict preserved

    Graduate-level science reasoning: multiple-choice questions written by domain PhDs.

    Comparable with caveatDo not treat a fixed two-point band as universal measurement uncertainty; it depends on run count and evaluation setup. Chain-of-thought, prompt, and tool policy must match. The small pool limits fine-grained conclusions, and frontier scores can be near ceiling; Artificial Analysis dropped it from its Intelligence Index in v4.3.

  • Source date2026-07-27
    Kimi K3 model card (vendor-reported, xhigh, Codex harness; orig OpenAI)

    Agentic terminal work: multi-step tasks executed in a sandboxed shell environment.

    Comparable with caveatNot comparable with Terminal-Bench 3.0 or 4.0 (different task sets) or v1; harness configuration (container, time limits) also shifts results.

  • Source date2026-06-01
    MiniMax vendor report: Terminus2, 8CPU/16GB sandbox, 2h, 128K generation budget; budget is not an API cap

    Agentic terminal work: multi-step tasks executed in a sandboxed shell environment.

    Comparable with caveatNot comparable with Terminal-Bench 3.0 or 4.0 (different task sets) or v1; harness configuration (container, time limits) also shifts results.

  • Source date2026-04-23
    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.

    Comparable with caveatSubset/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; prompts above 272K bill 2x input and 1.5x output for the full session in Standard/Batch/Flex. Cached input $0.50; short-context Batch $2.50/$15. Regional/FedRAMP processing adds 10% where eligible.

MiniMax M3: MiniMax Standard $0.30/$1.20 per MTok through 512K input; above 512K $0.60/$2.40. Cache reads $0.06/$0.12. Priority is 1.5x the applicable Standard rate. The page labels these current rates permanent 50% off; future tariffs are not established. Subscription-key Credits/M Plan and PAYG balance are separate.

FrontierRecord checked October 1, 2026

GPT-5.5

OpenAI’s April 2026 model for reasoning, coding and professional work, with a 1.05M-token context. Retained benchmark scores identify their source and configuration.

Best for

  • Reasoning
  • Agentic coding
  • Long-context work

Watch out

An older model, still listed in the API. GPT-5.5 retires from ChatGPT, Work and Codex on October 14, 2026; that notice does not affect the OpenAI API.

FrontierOpen weightsRecord checked October 2, 2026

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

Commercial checkpoint use requires Built with MiniMax M3 attribution and a one-time notice at or below $20M yearly revenue from the relevant products/services; above that threshold prior written authorization is required. Keep copyright/permission notices and follow prohibited-use terms. The checkpoint documents enabled/adaptive/disabled thinking. Hosted OpenAI compatibility defaults thinking on; Anthropic compatibility defaults it off and enables it with adaptive. Preview effort levels do not apply to M3. A hard output cap remains unverified.

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. This compares standard short-context token rates, not total cost per accepted task. Include input, cache, context tiers, schedules, retries and review in your pilot.

Run the model picker

Evidence confidence: Medium

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.
  • 2/5 core specs verified on both sides — Not published for at least one side: max output, parameter count, reasoning levels.
  • No directly aligned benchmark setup — No benchmark has matching cited sources, source dates, units, effort and recorded run details on both models.
  • Verified within the last 90 days — Older of the two catalog checks was 25 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.

Related comparisons

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. Estimate both input and output token volume for your workload. These are base rates; GPT-5.5: Standard $5/$30 per MTok; prompts above 272K bill 2x input and 1.5x output for the full session in Standard/Batch/Flex. Cached input $0.50; short-context Batch $2.50/$15. Regional/FedRAMP processing adds 10% where eligible. MiniMax M3: MiniMax Standard $0.30/$1.20 per MTok through 512K input; above 512K $0.60/$2.40. Cache reads $0.06/$0.12. Priority is 1.5x the applicable Standard rate. The page labels these current rates permanent 50% off; future tariffs are not established. Subscription-key Credits/M Plan and PAYG balance are separate.
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. Estimate both input and output token volume for your workload. These are base rates; GPT-5.5: Standard $5/$30 per MTok; prompts above 272K bill 2x input and 1.5x output for the full session in Standard/Batch/Flex. Cached input $0.50; short-context Batch $2.50/$15. Regional/FedRAMP processing adds 10% where eligible. MiniMax M3: MiniMax Standard $0.30/$1.20 per MTok through 512K input; above 512K $0.60/$2.40. Cache reads $0.06/$0.12. Priority is 1.5x the applicable Standard rate. The page labels these current rates permanent 50% off; future tariffs are not established. Subscription-key Credits/M Plan and PAYG balance are separate.
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.
Should I use GPT-5.5 or MiniMax M3?
Both are classified in our frontier tier; this editorial grouping does not imply equal capability. GPT-5.5 is a candidate for reasoning; MiniMax M3 is a candidate for open-weight deployments. Compare accuracy, tool reliability, latency and cost on representative tasks.
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.
  • Deployment: MiniMax M3 publishes weights you can self-host; the other is API-only.
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