GPT-5.6 Luna vs MiniMax M3.1 Flash

Updated October 19, 2026. Rank says MiniMax M3.1 Flash is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

MiniMax M3.1 Flash has the higher public point estimate, 67.05 versus 64.38. Their conditional score ranges overlap. These ranges do not establish rank confidence. 3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

GPT-5.6 Luna

OpenAI

64.38/100

Supported · Public rank #40

90% interval 59.4–69.4

Model B

MiniMax M3.1 Flash

MiniMax

67.05/100

Estimated · Public rank #28

Conditional range 52.7–81.4

Shared results: 3
GPT-5.6 Luna only: 29
MiniMax M3.1 Flash only: 9
Like-for-like categories: 2 / 8

Supported: GPT-5.6 Luna · Estimated: MiniMax M3.1 Flash. Conditional ranges do not establish rank confidence.How 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.

Long documents
Prompts that approach the documented context limit
GPT-5.6 Luna
GPT-5.6 Luna has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
MiniMax M3.1 Flash
MiniMax M3.1 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
MiniMax M3.1 Flash
MiniMax M3.1 Flash 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
MiniMax M3.1 Flash
MiniMax M3.1 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Coding work
Code generation, repair, and software-engineering tasks
No clear pick
The like-for-like coding result is a practical tie on the public lane (within 0.5 points).
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
MiniMax M3.1 Flash is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited

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.

Pick the job you would use them for

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

61.6GPT-5.6 Luna61.3MiniMax M3.1 Flash

Like-for-like · BenchAlign v5.8

GPT-5.6 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 leaderboard Shortlist models for this job

Same basis rules as the category table below

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.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

OSWorld 2.0Agentic
Normalized gap 16.1
GPT-5.6 Luna:45.6%
MiniMax M3.1 Flash:61.7%
GPT-5.6 Luna source MiniMax M3.1 Flash source

Bars run 0–100 on each benchmark’s normalized display scale

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-5.6 Luna: 61.6; Supported · #16/151
MiniMax M3.1 Flash: 61.3; Supported · #17/151
Basis: BenchAlign v5.8 lane · 8 vs 3 public rows
Reading: Practical tie

Knowledge

Like-for-like

GPT-5.6 Luna: 64.8; Supported · #38/186
MiniMax M3.1 Flash: 73.2; Supported · #14/186
Basis: BenchAlign v5.8 lane · 6 vs 1 public rows
Reading: MiniMax M3.1 Flash leads · intervals overlap

Agentic

Directional only

GPT-5.6 Luna: 56.0; Supported · #40/130
MiniMax M3.1 Flash: 63.9; Estimated · #19/130
Basis: BenchAlign v5.8 lane · 10 vs 6 public rows
Reading: Directional only

Multimodal

Directional only

GPT-5.6 Luna: 79.6; #19/55
MiniMax M3.1 Flash: 73.5; #30/55
Basis: Provisional lane · 1 vs 1 weighted rows
Reading: Directional only

Reasoning

Not comparable

GPT-5.6 Luna: 58.2; #25/26
MiniMax M3.1 Flash: 80.6; Unranked · 2 rankable rows
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GPT-5.6 Luna: Not ranked
MiniMax M3.1 Flash: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

GPT-5.6 Luna: Not ranked
MiniMax M3.1 Flash: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GPT-5.6 Luna: 84.0; Unranked · 3 rankable rows
MiniMax M3.1 Flash: Not ranked
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GPT-5.6 Luna and MiniMax M3.1 Flash
Category	GPT-5.6 Luna	MiniMax M3.1 Flash	Basis	Reading
Coding	61.6Supported · #16/151	61.3Supported · #17/151	Like-for-likeBenchAlign v5.8 lane · 8 vs 3 public rows	Practical tie
Knowledge	64.8Supported · #38/186	73.2Supported · #14/186	Like-for-likeBenchAlign v5.8 lane · 6 vs 1 public rows	MiniMax M3.1 Flash leads · intervals overlap
Agentic	56.0Supported · #40/130	63.9Estimated · #19/130	Directional onlyBenchAlign v5.8 lane · 10 vs 6 public rows	Directional only
Multimodal	79.6#19/55	73.5#30/55	Directional onlyProvisional lane · 1 vs 1 weighted rows	Directional only
Reasoning	58.2#25/26	80.6Unranked · 2 rankable rows	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	84.0Unranked · 3 rankable rows	Not ranked	Not comparableProvisional lane · 2 vs 0 weighted rows	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-5.6 Luna: $0.0008; Fits in one request
MiniMax M3.1 Flash: $0.00045; Fits in one request

MiniMax M3.1 Flash has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

GPT-5.6 Luna: $0.0136; Fits in one request
MiniMax M3.1 Flash: $0.0093; Fits in one request

MiniMax M3.1 Flash 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-5.6 Luna: $0.02; Fits in one request
MiniMax M3.1 Flash: $0.012; Fits in one request

MiniMax M3.1 Flash has the lower modeled cost

Costs use the listed standard API rates.

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.

SpecificationGPT-5.6 LunaMiniMax M3.1 Flash

Context window

Maximum documented context; output-token limits may be lower.

GPT-5.6 Luna

1.05M

OpenAI model catalog

MiniMax M3.1 Flash

MiniMax: M3.1 Flash launch post

API model ID

GPT-5.6 Luna

gpt-5.6-luna

OpenAI model catalog

MiniMax M3.1 Flash

MiniMax-M3.1-Flash

MiniMax: M3.1 Flash launch post

Cached-input rate

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

GPT-5.6 Luna

$0.02 per 1M cached input tokens

OpenAI pricing

MiniMax M3.1 Flash

$0.015 per 1M cached input tokens

MiniMax: M3.1 Flash launch post

Documented inputs

GPT-5.6 Luna

text, image

OpenAI model catalog

MiniMax M3.1 Flash

Not sourced

Documented outputs

GPT-5.6 Luna

text

OpenAI model catalog

MiniMax M3.1 Flash

Not sourced

Provider availability

GPT-5.6 Luna

Generally Available · OpenAI Responses API

OpenAI model catalog

MiniMax M3.1 Flash

Not sourced

Reasoning profile

GPT-5.6 Luna

Reasoning

MiniMax M3.1 Flash

Reasoning

Weight access

GPT-5.6 Luna

Proprietary

MiniMax M3.1 Flash

Proprietary

License

GPT-5.6 Luna

Proprietary

MiniMax M3.1 Flash

Proprietary

Release date

GPT-5.6 Luna

2026-07-09

MiniMax M3.1 Flash

2026-10-19

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: MiniMax M3.1 Flash has the higher public point estimate, 67.05 versus 64.38. Their conditional score ranges overlap. These ranges do not establish rank confidence.
Workload cost: Repository review: $0.0136 vs $0.0093. Cache-heavy agent loop: $0.02 vs $0.012.
Context tradeoff: GPT-5.6 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-5.6 Luna or MiniMax M3.1 Flash?

MiniMax M3.1 Flash has the higher public point estimate, 67.05 versus 64.38. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, GPT-5.6 Luna or MiniMax M3.1 Flash?

The like-for-like coding row is a practical tie on the public lane, 61.6 against 61.3, inside the 0.5-point band BenchLM treats as level.

Which is better for agentic tasks, GPT-5.6 Luna or MiniMax M3.1 Flash?

MiniMax M3.1 Flash scores higher for agentic tasks on the public lane, 63.9 to 56. MiniMax M3.1 Flash 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-5.6 Luna or MiniMax M3.1 Flash?

For the stated presets, chat costs $0.0008 on GPT-5.6 Luna and $0.00045 on MiniMax M3.1 Flash; repository review costs $0.0136 and $0.0093; the cache-heavy agent loop costs $0.02 and $0.012. Costs use the listed standard API rates.

Which has the larger context window, GPT-5.6 Luna or MiniMax M3.1 Flash?

GPT-5.6 Luna has the larger documented context window: 1.05M, compared with 1M.

Benchmark evidence

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

Browse raw public benchmark evidence41 rows

Agentic

Terminal-Bench 3.0
GPT-5.6 Luna14.3%
Source
MiniMax M3.1 Flash—
Not directly comparable
Terminal-Bench 2.1
GPT-5.6 Luna84.7%
Source
MiniMax M3.1 Flash—
Not directly comparable
BrowseComp
GPT-5.6 Luna83.3%
Source
MiniMax M3.1 Flash—
Not directly comparable
OSWorld 2.0
GPT-5.6 Luna45.6%
Source
MiniMax M3.1 Flash61.7%
Source
MiniMax M3.1 Flash leads this result
CyberGym
GPT-5.6 Luna77.9%
Source
MiniMax M3.1 Flash82.2%
Source
MiniMax M3.1 Flash leads this result
ExploitGym
GPT-5.6 Luna12.4%
Source
MiniMax M3.1 Flash—
Not directly comparable
Toolathlon
GPT-5.6 Luna53.4%
Source
MiniMax M3.1 Flash—
Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.6 Luna79.0%
Source
MiniMax M3.1 Flash—
Not directly comparable
ApprenticeBench
GPT-5.6 Luna7%
Source
MiniMax M3.1 Flash—
Not directly comparable
MazeBench
GPT-5.6 Luna0%
Source
MiniMax M3.1 Flash—
Not directly comparable
AutomationBench
GPT-5.6 Luna—
MiniMax M3.1 Flash62.2%
Source
Not directly comparable
BankerToolBench
GPT-5.6 Luna—
MiniMax M3.1 Flash76.2%
Source
Not directly comparable
Terminal-Bench 4.0
GPT-5.6 Luna—
MiniMax M3.1 Flash38.10%
Source
Not directly comparable
Agents' Last Exam
GPT-5.6 Luna—
MiniMax M3.1 Flash29.6%
Source
Not directly comparable

Coding

SWE-bench Pro
GPT-5.6 Luna62.7%
Source
MiniMax M3.1 Flash—
Not directly comparable
Terminal-Bench 2.1
GPT-5.6 Luna84.7%
Source
MiniMax M3.1 Flash—
Not directly comparable
DeepSWE
GPT-5.6 Luna67.2%
Source
MiniMax M3.1 Flash73.7%
Source
MiniMax M3.1 Flash leads this result
FrontierCode 1.1 Extended
GPT-5.6 Luna55.1%
Source
MiniMax M3.1 Flash—
Not directly comparable
CursorBench 3.2
GPT-5.6 Luna61.1%
Source
MiniMax M3.1 Flash—
Not directly comparable
VulcanBench v3
GPT-5.6 Luna85.5%
Source
MiniMax M3.1 Flash—
Not directly comparable
SWE-bench (Vals)
GPT-5.6 Luna93.0%
Source
MiniMax M3.1 Flash—
Not directly comparable
CursorBench 4.0
GPT-5.6 Luna35.9%
Source
MiniMax M3.1 Flash—
Not directly comparable
PostTrainBench v1.1
GPT-5.6 Luna—
MiniMax M3.1 Flash39.3%
Source
Not directly comparable
NL2Repo
GPT-5.6 Luna—
MiniMax M3.1 Flash61.2%
Source
Not directly comparable

Reasoning

ARC-AGI-2
GPT-5.6 Luna59.5%
Source
MiniMax M3.1 Flash—
Not directly comparable
ARC-AGI-3
GPT-5.6 Luna0.2%
Source
MiniMax M3.1 Flash—
Not directly comparable
ARC-AGI-1
GPT-5.6 Luna88.00%
Source
MiniMax M3.1 Flash—
Not directly comparable

Multimodal

MMMU-Pro
GPT-5.6 Luna78.4%
Source
MiniMax M3.1 Flash—
Not directly comparable
MMMU-Pro w/ Python
GPT-5.6 Luna79.5%
Source
MiniMax M3.1 Flash—
Not directly comparable
BabyVision w/ Python
GPT-5.6 Luna—
MiniMax M3.1 Flash91.0%
Source
Not directly comparable
CharXiv
GPT-5.6 Luna—
MiniMax M3.1 Flash82.5%
Source
Not directly comparable

Knowledge

GPQA
GPT-5.6 Luna92.3%
Source
MiniMax M3.1 Flash—
Not directly comparable
GPQA-D
GPT-5.6 Luna92.3%
Source
MiniMax M3.1 Flash—
Not directly comparable
HealthBench Professional
GPT-5.6 Luna55.7%
Source
MiniMax M3.1 Flash—
Not directly comparable
HealthBench Hard
GPT-5.6 Luna32.0%
Source
MiniMax M3.1 Flash—
Not directly comparable
GPQA Diamond (Vals)
GPT-5.6 Luna91.7%
Source
MiniMax M3.1 Flash—
Not directly comparable
MMLU-Pro (Vals)
GPT-5.6 Luna86.0%
Source
MiniMax M3.1 Flash—
Not directly comparable
HLE w/o tools
GPT-5.6 Luna—
MiniMax M3.1 Flash43.4%
Source
Not directly comparable

Math

FrontierMath (legacy)
GPT-5.6 Luna78.6%
Source
MiniMax M3.1 Flash—
Not directly comparable
FrontierMath v2 (Tiers 1-3)
GPT-5.6 Luna78.600%
Source
MiniMax M3.1 Flash—
Not directly comparable
FrontierMath v2 (Tier 4)
GPT-5.6 Luna58.500%
Source
MiniMax M3.1 Flash—
Not directly comparable

41 public results · 3 shared

Related comparisons

Compare API pricing Read the methodology Open the model selector

Watch GPT-5.6 Luna vs MiniMax M3.1 Flash

One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.

Read a sample issue

Join 5,600+ readers.

Last updated October 19, 2026

GPT-5.6 Luna vs MiniMax M3.1 Flash

Decision reading

Which one for your work

Long documents

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Coding work

Agentic work

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Coding

Knowledge

Agentic

Multimodal

Reasoning

Multilingual

Instruction following

Math

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

If you already use one of these models

Questions

Which is better, GPT-5.6 Luna or MiniMax M3.1 Flash?

Which is better for coding, GPT-5.6 Luna or MiniMax M3.1 Flash?

Which is better for agentic tasks, GPT-5.6 Luna or MiniMax M3.1 Flash?

Which costs less, GPT-5.6 Luna or MiniMax M3.1 Flash?

Which has the larger context window, GPT-5.6 Luna or MiniMax M3.1 Flash?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Math

Related comparisons

Watch GPT-5.6 Luna vs MiniMax M3.1 Flash