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Model comparison

GPT-5.6 Terra vs MiniMax M2.5

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

Catalog record checked September 26, 2026Individual provider fields may changeEvidence confidence: High — see receipts below

OpenAI

GPT-5.6 Terra

Balanced

vs

MiniMax

MiniMax M2.5

Balanced · Open weights

AI model capability comparison
SpecificationGPT-5.6 TerraMiniMax M2.5
ProviderOpenAIMiniMax
TierBalancedBalanced
Context windowWinner: 1.05M205K
Max output128KNot verifiedUnverified
Input / 1M tokens$2Winner: $0.30
Output / 1M tokens$12Winner: $1.20
WeightsClosedOpen
Parametersmid-tier reasoning modelopen MoE
Reasoning levelsnone, low, medium, high, xhigh, maxlow, high, max
Modalitiestext, imagetext
API model idgpt-5.6-terraNot publishedUnverified
ReleasedJuly 9, 2026February 12, 2026
Artificial Analysis Intelligence Index [max] (2026-09-26)42.1Not verifiedUnverified
Artificial Analysis Intelligence Index (2026-09-26)Not verifiedUnverified22.8
SWE-bench Verified (2026-09-01)95.4Not verifiedUnverified
Terminal-Bench 2.1 (2026-07-09)87.4Not verifiedUnverified
LiveBench (2026-09-05)77.9Not 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

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

  • 2026-09-26
    Artificial Analysis (AA-estimated)

    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 measuredAt.

  • 2026-09-05
    LiveBench official leaderboard (benchmark-owned), max effort

    Contamination-resistant general capability across reasoning, coding, math, data analysis, and language, with monthly question refreshes.

    Comparable with caveatRolling question set: observations months apart measure different question mixes. Record the measurement date and compare within ~1 month windows.

  • 2026-09-01
    vals.ai SWE-bench Verified leaderboard (independent, mini-swe-agent bash-only) (independent; official SWE-bench not published)

    Real GitHub issue resolution: does the model's patch pass the hidden tests.

    Comparable with caveatPost-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.

  • 2026-07-09
    OpenAI GPT-5.6 announcement (vendor; chart transcribed by Vellum)

    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.

Pricing tiers

GPT-5.6 Terra: Launch list was $2.50/$15; cut to $2/$12 on 2026-07-30. Above 272K input the whole request bills 2x input / 1.5x output. Cached input $0.20/MTok; cache writes 1.25x input; batch $1/$6.

MiniMax M2.5: $0.30/$1.20 per MTok on MiniMax's pay-as-you-go API (cache read $0.03). MiniMax lists M2.5 as a legacy model. Open weights (community license).

BalancedRecord checked September 26, 2026

GPT-5.6 Terra

GPT-5.6 Terra delivers roughly GPT-5.5-class quality at about half the cost, with the full 1.05M-token context.

Best for

  • Cost-sensitive production
  • Mixed reasoning workloads
  • High-volume agents

Watch out

GPT-6 Sol ($2/$10) now costs less than Terra ($2/$12) and scores higher — route frontier work to GPT-6 Sol. Terra has no named successor and is still served.

BalancedOpen weightsRecord checked September 26, 2026

MiniMax M2.5

MiniMax M2.5 is a legacy open-weight MiniMax MoE with a 204,800-token context.

Best for

  • Open-weight deployments
  • Multimodal

Watch out

Legacy: MiniMax's current models are M3 (1M context, multimodal) and M2.7 at the same $0.30/$1.20 price.

When the cheaper one wins

MiniMax M2.5 is cheaper on output at $1.20 per million tokens against $12 for GPT-5.6 Terra — about 10×. 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 picker

Evidence 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.
  • 4/5 core specs verified on both sides — Not published for at least one side: max output.
  • 1 shared named benchmark with differing scores — Measured on: Artificial Analysis Intelligence Index.
  • Verified within the last 90 days — Newest catalog check was 2 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-5.6 Terra · MiniMax M2.5

Common questions

GPT-5.6 Terra vs MiniMax M2.5

Answered from the verified figures on this page rather than general guidance.

Is GPT-5.6 Terra or MiniMax M2.5 cheaper for input?
MiniMax M2.5 is cheaper at $0.30 per million input tokens, against $2 for GPT-5.6 Terra — roughly 6.7× 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-5.6 Terra has tiered pricing: Launch list was $2.50/$15; cut to $2/$12 on 2026-07-30. Above 272K input the whole request bills 2x input / 1.5x output. Cached input $0.20/MTok; cache writes 1.25x input; batch $1/$6. MiniMax M2.5 has tiered pricing: $0.30/$1.20 per MTok on MiniMax's pay-as-you-go API (cache read $0.03). MiniMax lists M2.5 as a legacy model. Open weights (community license).
Is GPT-5.6 Terra or MiniMax M2.5 cheaper for output?
MiniMax M2.5 is cheaper at $1.20 per million output tokens, against $12 for GPT-5.6 Terra — roughly 10× 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-5.6 Terra has tiered pricing: Launch list was $2.50/$15; cut to $2/$12 on 2026-07-30. Above 272K input the whole request bills 2x input / 1.5x output. Cached input $0.20/MTok; cache writes 1.25x input; batch $1/$6. MiniMax M2.5 has tiered pricing: $0.30/$1.20 per MTok on MiniMax's pay-as-you-go API (cache read $0.03). MiniMax lists M2.5 as a legacy model. Open weights (community license).
Which has the larger context window, GPT-5.6 Terra or MiniMax M2.5?
GPT-5.6 Terra accepts 1.05M tokens against 205K for MiniMax M2.5. This only matters if you routinely send very long documents or large codebases.
Do GPT-5.6 Terra and MiniMax M2.5 support the same reasoning levels?
GPT-5.6 Terra exposes none, low, medium, high, xhigh, max, while MiniMax M2.5 exposes low, high, max.
Should I use GPT-5.6 Terra or MiniMax M2.5?
Both sit in the balanced tier, so the choice usually comes down to price and context rather than capability. GPT-5.6 Terra suits cost-sensitive production; MiniMax M2.5 suits open-weight deployments.
Can I self-host GPT-5.6 Terra or MiniMax M2.5?
MiniMax M2.5 publishes open weights, but self-hosting and third-party provider access depend on the licence, hardware, serving support, and availability. GPT-5.6 Terra 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 M2.5 costs $0.30 per 1M tokens versus $2 for GPT-5.6 Terra — a 6.7x difference at the headline tier.
  • Context: GPT-5.6 Terra takes 1.05M against 205K for MiniMax M2.5 — only decisive if your prompts approach the smaller window.
  • Deployment: MiniMax M2.5 publishes weights you can self-host; the other is API-only.
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