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GPT-6 Luna vs MiniMax M2.7

Updated November 6, 2026. Rank says GPT-6 Luna is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead. 0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
OpenAI logo
GPT-6 Luna

OpenAI

62.59/100

Supported · Public rank #43

90% interval 52.0–73.2

Model B
MiniMax logo
MiniMax M2.7

MiniMax

47.89/100

Supported · Public rank #108

90% interval 36.3–59.5

Shared results
0
GPT-6 Luna only
23
MiniMax M2.7 only
29
Like-for-like categories
2 / 8
Supported: GPT-6 Luna and MiniMax M2.7How the comparison works

Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

  • Coding work

    Code generation, repair, and software-engineering tasks

    GPT-6 Luna

    GPT-6 Luna has the higher public coding point estimate, 51.8 to 35, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    GPT-6 Luna

    GPT-6 Luna has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    GPT-6 Luna

    GPT-6 Luna has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates
Show secondary and unsupported calls

  • Repository review cost

    50K fresh input + 3K output tokens

    GPT-6 Luna

    GPT-6 Luna has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    GPT-6 Luna is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    Confidence: limited

  • Cache-heavy agent loop cost

    200K cached + 20K fresh input + 10K output tokens

    Not enough matched evidence

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. MiniMax M2.7 does not fit this workload in one request. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

51.8GPT-6 Luna35.0MiniMax M2.7

Like-for-like · BenchAlign v5.8

GPT-6 Luna has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboardShortlist models for this job
Same basis rules as the category table below

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Coding

Like-for-like
GPT-6 Luna
51.8
Supported · #42/157
MiniMax M2.7
35.0
Supported · #87/157
Basis
BenchAlign v5.8 lane · 3 vs 12 public rows
Reading
GPT-6 Luna leads · intervals overlap

Knowledge

Like-for-like
GPT-6 Luna
63.3
Supported · #40/178
MiniMax M2.7
45.9
Supported · #93/178
Basis
BenchAlign v5.8 lane · 5 vs 5 public rows
Reading
GPT-6 Luna leads · intervals overlap

Agentic

Directional only
GPT-6 Luna
53.5
Estimated · #45/131
MiniMax M2.7
32.9
Supported · #95/131
Basis
BenchAlign v5.8 lane · 5 vs 8 public rows
Reading
Directional only

Reasoning

Not comparable
GPT-6 Luna
57.5
#22/25
MiniMax M2.7
77.1
Unranked · 2 rankable rows
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
GPT-6 Luna
81.9
Unranked · 1 rankable row
MiniMax M2.7
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
GPT-6 Luna
Not ranked
MiniMax M2.7
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
GPT-6 Luna
Not ranked
MiniMax M2.7
93.0
#14/157
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
GPT-6 Luna
80.2
Unranked · 2 rankable rows
MiniMax M2.7
46.6
Unranked · 4 rankable rows
Basis
Provisional lane · 2 vs 2 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

GPT-6 Luna
$0.00035
Fits in one request
MiniMax M2.7
$0.0009
Fits in one request

GPT-6 Luna has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

GPT-6 Luna
$0.0065
Fits in one request
MiniMax M2.7
$0.0186
Fits in one request

GPT-6 Luna has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

GPT-6 Luna
$0.009
Fits in one request
MiniMax M2.7
$0.078
Does not fit in one request
Cached input priced at the published list-input rate

MiniMax M2.7 does not fit this workload in one request. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate.

Cached input falls back to the list input rate only where a cached rate is unpublished

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

GPT-6 Luna

$0.01 per 1M cached input tokens

OpenAI GPT-6 Luna model documentation

MiniMax M2.7

Not published

Provider availability

GPT-6 Luna

Generally Available · OpenAI Responses API, OpenAI Chat Completions API, ChatGPT Work, Codex

OpenAI GPT-6 Sol and Luna launch

MiniMax M2.7

Not sourced

Reasoning profile

GPT-6 Luna

Reasoning

MiniMax M2.7

Non-Reasoning

Weight access

GPT-6 Luna

Proprietary

MiniMax M2.7

Open Weight

License

GPT-6 Luna

Proprietary

MiniMax M2.7

Open Weight

Release date

GPT-6 Luna

2026-09-22

MiniMax M2.7

2026-03-18

If you already use one of these models

Deployment change
The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
Repository review: $0.0065 vs $0.0186. Cache-heavy agent loop: $0.009 vs $0.078.
Context tradeoff
GPT-6 Luna has the larger documented window (1.05M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, GPT-6 Luna or MiniMax M2.7?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, GPT-6 Luna or MiniMax M2.7?

GPT-6 Luna has the higher public coding point estimate, 51.8 to 35, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which is better for agentic tasks, GPT-6 Luna or MiniMax M2.7?

GPT-6 Luna scores higher for agentic tasks on the public lane, 53.5 to 32.9. GPT-6 Luna is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, GPT-6 Luna or MiniMax M2.7?

For the stated presets, chat costs $0.00035 on GPT-6 Luna and $0.0009 on MiniMax M2.7; repository review costs $0.0065 and $0.0186; the cache-heavy agent loop costs $0.009 and $0.078. MiniMax M2.7 does not fit this workload in one request. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, GPT-6 Luna or MiniMax M2.7?

GPT-6 Luna has the larger documented context window: 1.05M, compared with 200K.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

GPT-6 Luna
API / mo$450
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
MiniMax M2.7
API / mo$1,125
Self-host / mo$6,990
Break-even409M/day
Model the full break-even

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence52 rows

Agentic

  • ExploitGym

    GPT-6 Luna11.6%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Hermes Index

    GPT-6 Luna33.9%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Hermes Bench

    GPT-6 Luna70.5%
    Source
    MiniMax M2.7—

    Not directly comparable

  • MazeBench

    GPT-6 Luna0%
    Source
    MiniMax M2.7—

    Not directly comparable

  • OSWorld 2.1 Offline

    GPT-6 Luna48.9%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Terminal-Bench 2.0

    GPT-6 Luna—
    MiniMax M2.757%
    Source

    Not directly comparable

  • τ³-bench results

    GPT-6 Luna—
    MiniMax M2.767.6%
    Source

    Not directly comparable

  • Toolathlon

    GPT-6 Luna—
    MiniMax M2.746.3%
    Source

    Not directly comparable

  • MLE-Bench Lite

    GPT-6 Luna—
    MiniMax M2.766.6%
    Source

    Not directly comparable

  • MM-ClawBench

    GPT-6 Luna—
    MiniMax M2.762.7%
    Source

    Not directly comparable

  • Claw-Eval

    GPT-6 Luna—
    MiniMax M2.748.7%
    Source

    Not directly comparable

  • Gert Labs

    GPT-6 Luna—
    MiniMax M2.740.40%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    GPT-6 Luna—
    MiniMax M2.748.7%
    Source

    Not directly comparable

Coding

  • DeepSWE

    GPT-6 Luna66.6%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Bug Hunt Bench

    GPT-6 Luna18.3 fixes
    Source
    MiniMax M2.7—

    Not directly comparable

  • FrontierCode 2.0

    GPT-6 Luna21.9%
    Source
    MiniMax M2.7—

    Not directly comparable

  • SWE-bench Verified*

    GPT-6 Luna—
    MiniMax M2.775.4%
    Source

    Not directly comparable

  • SWE-bench Pro

    GPT-6 Luna—
    MiniMax M2.756.2%
    Source

    Not directly comparable

  • SWE-Rebench

    GPT-6 Luna—
    MiniMax M2.751.9%
    Source

    Not directly comparable

  • SWE Multilingual

    GPT-6 Luna—
    MiniMax M2.776.5%
    Source

    Not directly comparable

  • Multi-SWE Bench

    GPT-6 Luna—
    MiniMax M2.752.7%
    Source

    Not directly comparable

  • VIBE-Pro

    GPT-6 Luna—
    MiniMax M2.755.6%
    Source

    Not directly comparable

  • NL2Repo

    GPT-6 Luna—
    MiniMax M2.739.8%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    GPT-6 Luna—
    MiniMax M2.757.0%
    Source

    Not directly comparable

  • Vibe Code Bench

    GPT-6 Luna—
    MiniMax M2.727.04%
    Source

    Not directly comparable

  • React Native Evals

    GPT-6 Luna—
    MiniMax M2.771.4%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    GPT-6 Luna—
    MiniMax M2.779.9%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    GPT-6 Luna—
    MiniMax M2.773.8%
    Source

    Not directly comparable

Reasoning

  • BioBasicsBench

    GPT-6 Luna37.6%
    Source
    MiniMax M2.7—

    Not directly comparable

  • ARC-AGI-1

    GPT-6 Luna86.67%
    Source
    MiniMax M2.7—

    Not directly comparable

  • ARC-AGI-2

    GPT-6 Luna59.3%
    Source
    MiniMax M2.7—

    Not directly comparable

  • ARC-AGI-3

    GPT-6 Luna0.1%
    Source
    MiniMax M2.7—

    Not directly comparable

  • ARC-AGI-3 Provider Adapter

    GPT-6 Luna0.59%
    Source
    MiniMax M2.7—

    Not directly comparable

Multimodal

  • ExtractBench Pro

    GPT-6 Luna0%
    Source
    MiniMax M2.7—

    Not directly comparable

  • ExtractBench (LlamaIndex)

    GPT-6 Luna91.01%
    Source
    MiniMax M2.7—

    Not directly comparable

  • ParseBench

    GPT-6 Luna64.13%
    Source
    MiniMax M2.7—

    Not directly comparable

Knowledge

  • HealthBench (raw)

    GPT-6 Luna50.0%
    Source
    MiniMax M2.7—

    Not directly comparable

  • HealthBench (length-adjusted)

    GPT-6 Luna54.5%
    Source
    MiniMax M2.7—

    Not directly comparable

  • HealthBench Professional

    GPT-6 Luna60.8%
    Source
    MiniMax M2.7—

    Not directly comparable

  • HealthBench Professional (raw)

    GPT-6 Luna61.2%
    Source
    MiniMax M2.7—

    Not directly comparable

  • HealthBench Hard

    GPT-6 Luna31.4%
    Source
    MiniMax M2.7—

    Not directly comparable

  • HLE

    GPT-6 Luna—
    MiniMax M2.728%
    Source

    Not directly comparable

  • GPQA-D

    GPT-6 Luna—
    MiniMax M2.787.0%
    Source

    Not directly comparable

  • MMLU-Pro (Arcee)

    GPT-6 Luna—
    MiniMax M2.780.8%
    Source

    Not directly comparable

  • GPQA Diamond (Vals)

    GPT-6 Luna—
    MiniMax M2.786.6%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    GPT-6 Luna—
    MiniMax M2.780.4%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    GPT-6 Luna78.947%
    Source
    MiniMax M2.7—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    GPT-6 Luna56.098%
    Source
    MiniMax M2.7—

    Not directly comparable

  • AIME25 (Arcee)

    GPT-6 Luna—
    MiniMax M2.780.0%
    Source

    Not directly comparable

  • AIME26

    GPT-6 Luna—
    MiniMax M2.789.8%
    Source

    Not directly comparable

  • HMMT Nov 2025

    GPT-6 Luna—
    MiniMax M2.781.0%
    Source

    Not directly comparable

  • HMMT Feb 2026

    GPT-6 Luna—
    MiniMax M2.772.7%
    Source

    Not directly comparable

52 public results · 0 shared

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Last updated November 6, 2026