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Model comparison · accepted catalogue facts 2026-10-16

MiniMax M2.7 vs MiniMax M3

MiniMax M3 has an equal-or-higher measured score and an equal-or-lower estimated price. No known model-capability loss from MiniMax M2.7; endpoint and task checks still apply. The reported score intervals do not overlap; this is not a task-specific success guarantee.

Editorial comparison: Modality and reasoning change; licence caution Inclusion does not recommend either model.

MiniMax M2.7

LMArena general · Elo · higher is better

Scale starts at 1040 Elo

1404.5

Balanced effective USD / 1M tokens · lower is better

$0.525/M

MiniMax M3

LMArena general · Elo · higher is better

Scale starts at 1040 Elo

1433.4

Balanced effective USD / 1M tokens · lower is better

$0.485/M

In-Depth Analysis

MiniMax M2.7 vs MiniMax M3: Measured Technical Breakdown

Comprehensive comparison across measured human-preference evaluation, effective production token costs, architectural limits, and migration trade-offs.

Measured Capability (LMArena)
MiniMax M3 leads by 28.9 Elo

MiniMax M2.7: 1404.5 ±3.33 · MiniMax M3: 1433.4 ±3.83 Statistically significant preference separation

Cost Comparison Across Canonical Production Workloads

Rates represent effective cost per 1M tokens under standard token blends. Cached prompt reads are factored according to workload context reuse.

Workload & RatioMiniMax M2.7MiniMax M3Cost DifferenceAdvantage
Balanced 3 tokens in per 1 out$0.525 /1M tokens$0.485 /1M tokens$0.040 (8% diff)MiniMax M3 costs less
Summarise Huge input, tiny output$0.277 /1M tokens$0.257 /1M tokens$0.019 (7% diff)MiniMax M3 costs less
Chat Long cached system prompt$0.588 /1M tokens$0.541 /1M tokens$0.047 (8% diff)MiniMax M3 costs less
Code gen Output dominates$0.821 /1M tokens$0.754 /1M tokens$0.067 (8% diff)MiniMax M3 costs less
Agentic Context replayed each turn$0.292 /1M tokens$0.270 /1M tokens$0.022 (8% diff)MiniMax M3 costs less

Architectural & Operational Specifications

SpecificationMiniMax M2.7MiniMax M3Operational Note
Context window204,800 tokens1,048,576 tokensMiniMax M3 has a 5.1× larger context window
Max output generation131,072 tokens512,000 tokensMiniMax M3 allows longer single-turn completions
Base input price$0.300 / 1M tokens$0.280 / 1M tokensMiniMax M3 input rate is cheaper
Base output price$1.20 / 1M tokens$1.10 / 1M tokensMiniMax M3 output rate is cheaper
Cached input price$0.060 / 1M tokens$0.056 / 1M tokensMiniMax M3 offers lower cached prompt cost
Reasoning & thinking tokensSupported; standard ratesSupported; standard rates—
Input modalitiestexttext, image, video—
Tool calling / function useSupportedSupported—
Open weights & licenceProprietary (API-only)Open weights (minimax-community)MiniMax M3 allows independent hosting or local deployment
Provider & reseller marketMiniMax · 6 provider offers (2.86× spread)MiniMax · 14 provider offers (2.68× spread)—

When to Choose MiniMax M2.7

  • Direct integration with MiniMax tooling and API features.
Risks when moving from MiniMax M3 → MiniMax M2.7:
  • Context window: 1,048,576 → 204,800 tokens.
  • Output limit: 512,000 → 131,072 tokens.
  • image input is not supported by the candidate.
  • video input is not supported by the candidate.

When to Choose MiniMax M3

  • Higher measured general capability (1433.4 Elo on human evaluation).
  • Larger context window (1,048,576 tokens) for long-document or repo-level context.
  • Higher maximum completion limit (512,000 tokens).
  • Open weights availability allows private self-hosting and on-premise execution.
  • Broader multimodal input support: text, image, video.
  • Direct integration with MiniMax tooling and API features.

Detailed Evidence Passports & Independent Audits

MiniMax M2.7 Full model card, pricing ladder & provider offers → MiniMax M3 Full model card, pricing ladder & provider offers → Alternatives to MiniMax M2.7 Better-and-cheaper dominance audit → Alternatives to MiniMax M3 Better-and-cheaper dominance audit →

Compare · model decision

Choose two or three models

Inspect the differences, estimate your workload and keep the evidence. All standard catalogue models are available, including unrated ones.

Catalogue facts
2026-10-16

2 / 3 selected · 0 requirements
Open public comparison link
Public workload and requirements

These settings are included in the share link. The fixed-request scenario and private evaluation data below are excluded.

A ↔ B

MiniMax M3 has an equal-or-higher measured score and an equal-or-lower estimated price. No known model-capability loss from MiniMax M2.7; endpoint and task checks still apply. The reported score intervals do not overlap; this is not a task-specific success guarantee.

Estimated blend: 75% input, 25% output; 0% cached input. Base context tier. Prompt length selects the billing tier. An unlisted cache-read rate uses that tier’s full input rate in this blend; missing required input or output rates prevent an estimate. The fixed-request estimate below validates actual context and output limits and requires a published cache-read rate when cache hits are assumed.

What changes between these models

Quality first, then estimated price. LMArena scores are human-preference measurements, not a benchmark of your application. A missing score is unrated.

  • MiniMax M2.7 · Meets the stated model requirements
  • MiniMax M3 · Meets the stated model requirements
Material differences and unknown evidence. Deltas compare each model with model A. Unknown facts remain visible.
Measured factA MiniMax M2.7B MiniMax M3
LMArena general score (Elo)1,404.51,433.4Δ vs A: +28.9 Elo
Score interval half-width (± Elo)3.333.83Δ vs A: +0.5 Elo
LMArena document score (Elo)Unknown1,434.8
Estimated effective USD / 1M tokens$0.525$0.485Δ vs A: -0.04 USD/M
Base input USD / 1M tokens$0.3$0.28Δ vs A: -0.02 USD/M
Base output USD / 1M tokens$1.2$1.1Δ vs A: -0.1 USD/M
Base cached-input USD / 1M tokens$0.06$0.056Δ vs A: -0.004 USD/M
Context window (tokens)204,8001,048,576Δ vs A: +843,776 tokens
Output limit (tokens)131,072512,000Δ vs A: +380,928 tokens
Input modalitiestexttext, image, video
Open weightsUnknownSupported
Recorded licenceUnknownminimax-community
Distinct sellers in the catalogue513Δ vs A: +8
Recorded retirement dateUnknownUnknown
6 shared measured facts
Shared measured facts. Deltas compare each model with model A. Unknown facts remain visible.
Measured factA MiniMax M2.7B MiniMax M3
Benchmark reasoning effortdefaultdefault
Tool useSupportedSupportedΔ vs A: 0
ReasoningSupportedSupportedΔ vs A: 0
Model providerMiniMaxMiniMax
Serving precision differs across offersSupportedSupportedΔ vs A: 0
Retirement evidenceNone announced; not a guaranteeNone announced; not a guarantee

Provider counts and precision flags describe catalogue offers. They do not establish the precision, availability, region, tool behaviour or limits of your chosen endpoint. Open each model’s provider table before switching.

Complete price ladders and billing conditions

MiniMax M2.7

Rates below are USD per 1M tokens. A threshold selects that whole input/output rate pair.

Input-token thresholdInputOutputCached input
Base, up to and including first threshold$0.3$1.2$0.06

MiniMax M3

Rates below are USD per 1M tokens. A threshold selects that whole input/output rate pair.

Input-token thresholdInputOutputCached input
Base, up to and including first threshold$0.28$1.1$0.056
Migration preflight · compare each candidate with model A

Keeping MiniMax M2.7 is a valid decision. No pairwise comparison certifies a drop-in replacement.

MiniMax M2.7 → MiniMax M3

No known model-capability loss

  • Provider endpoint, serving precision, availability and task success need your own verification. Catalogue model capabilities do not certify an endpoint.
Check alternatives to MiniMax M2.7

Estimate a fixed request shape

Private to this tab unless you save locally or explicitly export private details. These are estimates, not observed invoices. Cross-model token counts and task success are assumptions.

0.1 extra attempts means 10 extra full attempts per 100 requests. Cache writes, tool charges, taxes and unlisted charges are excluded.

  • MiniMax M2.7Estimated total: $1.2 1,000 full attempts · base rates
  • MiniMax M3Estimated total: $1.11 1,000 full attempts · base rates
Prompt and output sensitivity · sampled scenarios

Every point recalculates the entire request shape. Tier-boundary samples include one token below, at and above each threshold. Ordering between samples is not guaranteed. Lowest known cost excludes unknown estimates and is not a replacement recommendation.

Vary input; hold output, requests, cache and retries fixed.
Input tokensMiniMax M2.7MiniMax M3Coverage
2,000$1.2$1.11Lowest knownAll computed
4,096$1.8288$1.69688Lowest knownAll computed
32,768$10.4304$9.72504Lowest knownAll computed
131,072$39.9216$37.25016Lowest knownAll computed
Vary output; hold input, requests, cache and retries fixed.
Output tokensMiniMax M2.7MiniMax M3
128$0.7536$0.7008
2,048$3.0576$2.8128
8,192$10.4304$9.5712

Bring your observed evaluation results

JSON is parsed locally. Prompts, filenames, individual responses and extra fields are not retained. Import only aggregate measurements for selected models. Task/version labels do not establish that two evaluations used the same dataset.

Expected format
[{"model":"provider/exact-model-id","task":"support-answer","version":"rubric-1","attempts":100,"successes":84,"costUsd":2.4,"latencyMs":950}]

Use the exact catalogue ID. Replace the example values with observed measurements. Omit costUsd or latencyMs when they are unknown. No public score is inferred from these observations.

Keep the decision and its evidence

A portable record retains selected facts, complete tier ladders, public requirements and the publication manifest. It also embeds the full permitted public catalogue to verify the input artifact checksum. Files are larger for this reason (up to 2 MB); local storage is limited to 12 records. Team review is local to this browser; there is no hosted collaboration.

Saving is opt-in and includes private review fields. Shared comparison links never include them. Version 1 Check references remain separate and are not overwritten or guessed into this format.

Saved local decisions · 0

No version 2 decision records are stored in this browser.

Questions this page answers

Is MiniMax M2.7 a better-and-cheaper replacement for MiniMax M3?

MiniMax M3 has an equal-or-higher measured score and an equal-or-lower estimated price. No known model-capability loss from MiniMax M2.7; endpoint and task checks still apply. The reported score intervals do not overlap; this is not a task-specific success guarantee.

Which model is higher rated: MiniMax M2.7 or MiniMax M3?

MiniMax M3 scores higher on LMArena general (1433.4 ±3.8) compared to MiniMax M2.7 (1404.5 ±3.3), a margin of 28.9 Elo points. The measured confidence intervals do not overlap, demonstrating a statistically significant preference lead on human evaluations.

Which model is more cost-effective: MiniMax M2.7 or MiniMax M3?

On a standard balanced workload (3 prompt tokens per 1 generated token), MiniMax M3 costs $0.485 per 1M tokens versus $0.525 for MiniMax M2.7. MiniMax M2.7 is approximately 1.1× the price of MiniMax M3. Base input rates are $0.300 for MiniMax M2.7 and $0.280 for MiniMax M3; base output rates are $1.20 for MiniMax M2.7 and $1.10 for MiniMax M3.

How do their context windows and generation limits compare?

MiniMax M2.7 provides a 204,800-token context window with a maximum output limit of 131,072 tokens. MiniMax M3 provides a 1,048,576-token context window with a maximum output of 512,000 tokens. MiniMax M3 offers a 5.1× larger context capacity, making it suitable for larger repositories and multi-document workflows.

What capabilities are lost when switching between MiniMax M2.7 and MiniMax M3?

Switching from MiniMax M2.7 to MiniMax M3 causes no detected architectural capability loss. Conversely, moving from MiniMax M3 to MiniMax M2.7 involves: Context window: 1,048,576 → 204,800 tokens; Output limit: 512,000 → 131,072 tokens; image input is not supported by the candidate; video input is not supported by the candidate. Run test suites on your specific prompt templates and tool declarations before cutting over production traffic.

Can MiniMax M2.7 or MiniMax M3 be self-hosted or deployed privately?

MiniMax M3 provides open weights, enabling self-hosting on private GPU infrastructure, local quantization, and private data pipelines. Managed inference is also offered across third-party API providers (6 for MiniMax M2.7, 14 for MiniMax M3).

What is included in this comparison?

Accepted model capabilities, context and output limits, complete price-tier ladders, independent LMArena measurements and missing evidence. Provider endpoint precision and application success still need verification.

Can I compare a different workload or keep a decision record?

Yes. Change the public workload, prompt length and requirements in the comparison workspace. Fixed-request scenarios, aggregate evaluations and review notes stay local unless explicitly exported.

Evidence & Ask
Markdown for LLMs