GPT-5.6 Sol vs MiniMax M3

Updated September 24, 2026. Rank says GPT-5.6 Sol is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

GPT-5.6 Sol has the higher public score, 78.49 versus 54.86, and the 90% score intervals do not overlap. 11 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 Sol

OpenAI

78.49/100

Supported · Public rank #7

90% interval 75.6–81.4

Model B

MiniMax M3

MiniMax

54.86/100

Supported · Public rank #58

90% interval 46.2–63.5

Shared results: 11
GPT-5.6 Sol only: 26
MiniMax M3 only: 16
Like-for-like categories: 3 / 8

Supported: GPT-5.6 Sol and MiniMax M3How 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-5.6 Sol
GPT-5.6 Sol leads on the public coding lane, 71.6 to 39.2, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Agentic work
Tool use, computer use, and multi-step task completion
GPT-5.6 Sol
GPT-5.6 Sol leads on the public agentic lane, 69.6 to 39.9, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Long documents
Prompts that approach the documented context limit
GPT-5.6 Sol
GPT-5.6 Sol has the larger documented context window.
Confidence: documented

Show secondary and unsupported calls

Chat turn cost
1K fresh input + 500 output tokens
MiniMax M3
MiniMax M3 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
MiniMax M3 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K output tokens
MiniMax M3
MiniMax M3 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates

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.

71.6GPT-5.6 Sol39.2MiniMax M3

Like-for-like · BenchAlign v5.7

GPT-5.6 Sol leads the like-for-like coding row.

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 58.0
GPT-5.6 Sol:62.6%
MiniMax M3:4.6%
GPT-5.6 Sol source MiniMax M3 source
BrowseCompAgentic
Normalized gap 8.7
GPT-5.6 Sol:92.2%
MiniMax M3:83.5%
GPT-5.6 Sol source MiniMax M3 source
SWE-bench ProCoding
Normalized gap 5.6
GPT-5.6 Sol:64.6%
MiniMax M3:59%
GPT-5.6 Sol source MiniMax M3 source
MMMU-ProMultimodal
Normalized gap 4.9
GPT-5.6 Sol:83%
MiniMax M3:78.1%
GPT-5.6 Sol source MiniMax M3 source
MMLU-Pro (Vals)Knowledge
Normalized gap 4.9
GPT-5.6 Sol:89.1%
MiniMax M3:84.2%
GPT-5.6 Sol source MiniMax M3 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 v5.7 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.

Agentic

Like-for-like

GPT-5.6 Sol: 69.6; Supported · #7/105
MiniMax M3: 39.9; Supported · #45/105
Basis: BenchAlign v5.7 lane · 9 vs 9 public rows
Reading: GPT-5.6 Sol leads

Coding

Like-for-like

GPT-5.6 Sol: 71.6; Supported · #6/135
MiniMax M3: 39.2; Supported · #59/135
Basis: BenchAlign v5.7 lane · 12 vs 10 public rows
Reading: GPT-5.6 Sol leads

Knowledge

Like-for-like

GPT-5.6 Sol: 78.8; Supported · #7/158
MiniMax M3: 49.3; Supported · #59/158
Basis: BenchAlign v5.7 lane · 8 vs 2 public rows
Reading: GPT-5.6 Sol leads

Multimodal

Directional only

GPT-5.6 Sol: 87.6; #5/50
MiniMax M3: 52.0; #36/50
Basis: Provisional lane · 1 vs 2 weighted rows
Reading: Directional only

Instruction following

Directional only

GPT-5.6 Sol: 87.7; #28/124
MiniMax M3: 92.4; #5/124
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Directional only

Reasoning

Not comparable

GPT-5.6 Sol: 72.1; #8/19
MiniMax M3: 79.1; Unranked · 2 rankable rows
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GPT-5.6 Sol: Not ranked
MiniMax M3: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GPT-5.6 Sol: 96.8; Unranked · 3 rankable rows
MiniMax M3: Not ranked
Basis: Provisional lane · 2 vs 1 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GPT-5.6 Sol and MiniMax M3
Category	GPT-5.6 Sol	MiniMax M3	Basis	Reading
Agentic	69.6Supported · #7/105	39.9Supported · #45/105	Like-for-likeBenchAlign v5.7 lane · 9 vs 9 public rows	GPT-5.6 Sol leads
Coding	71.6Supported · #6/135	39.2Supported · #59/135	Like-for-likeBenchAlign v5.7 lane · 12 vs 10 public rows	GPT-5.6 Sol leads
Knowledge	78.8Supported · #7/158	49.3Supported · #59/158	Like-for-likeBenchAlign v5.7 lane · 8 vs 2 public rows	GPT-5.6 Sol leads
Multimodal	87.6#5/50	52.0#36/50	Directional onlyProvisional lane · 1 vs 2 weighted rows	Directional only
Instruction following	87.7#28/124	92.4#5/124	Directional onlyProvisional lane · 0 vs 0 weighted rows	Directional only
Reasoning	72.1#8/19	79.1Unranked · 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
Math	96.8Unranked · 3 rankable rows	Not ranked	Not comparableProvisional lane · 2 vs 1 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) 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 Sol: $0.014; Fits in one request
MiniMax M3: $0.0009; Fits in one request

MiniMax M3 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

GPT-5.6 Sol: $0.26; Fits in one request
MiniMax M3: $0.0186; Fits in one request

MiniMax M3 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 Sol: $0.36; Fits in one request
MiniMax M3: $0.03; Fits in one request

MiniMax M3 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 SolMiniMax M3

Context window

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

GPT-5.6 Sol

1.05M

OpenAI model catalog

MiniMax M3

API model ID

GPT-5.6 Sol

gpt-5.6-sol

OpenAI model catalog

MiniMax M3

Not sourced

Cached-input rate

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

GPT-5.6 Sol

$0.4 per 1M cached input tokens

OpenAI pricing

MiniMax M3

$0.06 per 1M cached input tokens

Documented inputs

GPT-5.6 Sol

text, image

OpenAI model catalog

MiniMax M3

Not sourced

Documented outputs

GPT-5.6 Sol

text

OpenAI model catalog

MiniMax M3

Not sourced

Provider availability

GPT-5.6 Sol

Generally Available · OpenAI Responses API

OpenAI model catalog

MiniMax M3

Not sourced

Reasoning profile

GPT-5.6 Sol

Reasoning

MiniMax M3

Non-Reasoning

Weight access

GPT-5.6 Sol

Proprietary

MiniMax M3

Open Weight

License

GPT-5.6 Sol

Proprietary

MiniMax M3

Open Weight

Release date

GPT-5.6 Sol

2026-07-09

MiniMax M3

2026-06-01

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: GPT-5.6 Sol has the higher public score, 78.49 versus 54.86, and the 90% score intervals do not overlap.
Workload cost: Repository review: $0.26 vs $0.0186. Cache-heavy agent loop: $0.36 vs $0.03.
Context tradeoff: GPT-5.6 Sol 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 Sol or MiniMax M3?

GPT-5.6 Sol has the higher public score, 78.49 versus 54.86, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, GPT-5.6 Sol or MiniMax M3?

GPT-5.6 Sol leads the public coding lane, 71.6 to 39.2, with Supported evidence for both models and non-overlapping 90% intervals.

Which is better for agentic tasks, GPT-5.6 Sol or MiniMax M3?

GPT-5.6 Sol leads the public agentic tasks lane, 69.6 to 39.9, with Supported evidence for both models and non-overlapping 90% intervals.

Which costs less, GPT-5.6 Sol or MiniMax M3?

For the stated presets, chat costs $0.014 on GPT-5.6 Sol and $0.0009 on MiniMax M3; repository review costs $0.26 and $0.0186; the cache-heavy agent loop costs $0.36 and $0.03. Costs use the listed standard API rates.

Which has the larger context window, GPT-5.6 Sol or MiniMax M3?

GPT-5.6 Sol 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 evidence53 rows

Agentic

Terminal-Bench 3.0
GPT-5.6 Sol34.6%
Source
MiniMax M3—
Not directly comparable
Terminal-Bench 2.1
GPT-5.6 Sol91.9%
Source
MiniMax M366.0%
Source
GPT-5.6 Sol leads this result
BrowseComp
GPT-5.6 Sol92.2%
Source
MiniMax M383.5%
Source
GPT-5.6 Sol leads this result
OSWorld 2.0
GPT-5.6 Sol62.6%
Source
MiniMax M34.6%
Source
GPT-5.6 Sol leads this result
CyberGym
GPT-5.6 Sol84.5%
Source
MiniMax M3—
Not directly comparable
ExploitGym
GPT-5.6 Sol33.7%
Source
MiniMax M3—
Not directly comparable
Toolathlon
GPT-5.6 Sol58%
Source
MiniMax M3—
Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.6 Sol85.8%
Source
MiniMax M353.6%
Source
GPT-5.6 Sol leads this result
ApprenticeBench
GPT-5.6 Sol26%
Source
MiniMax M3—
Not directly comparable
OSWorld-Verified
GPT-5.6 Sol—
MiniMax M370.1%
Source
Not directly comparable
MCP Atlas
GPT-5.6 Sol—
MiniMax M374.2%
Source
Not directly comparable
Claw-Eval
GPT-5.6 Sol—
MiniMax M374.5%
Source
Not directly comparable
BankerToolBench
GPT-5.6 Sol—
MiniMax M376.1%
Source
Not directly comparable
ResearchClawBench
GPT-5.6 Sol—
MiniMax M319.8%
Source
Not directly comparable

Coding

Bug Hunt Bench
GPT-5.6 Sol42 fixes
Source
MiniMax M3—
Not directly comparable
SWE-bench Pro
GPT-5.6 Sol64.6%
Source
MiniMax M359%
Source
GPT-5.6 Sol leads this result
Terminal-Bench 2.1
GPT-5.6 Sol91.9%
Source
MiniMax M366.0%
Source
GPT-5.6 Sol leads this result
DeepSWE
GPT-5.6 Sol72.7%
Source
MiniMax M3—
Not directly comparable
FrontierCode 1.1 Extended
GPT-5.6 Sol60.6%
Source
MiniMax M3—
Not directly comparable
FrontierSWE v2
GPT-5.6 Sol32.2%
Source
MiniMax M3—
Not directly comparable
cursorBench32
GPT-5.6 Sol67.2%
Source
MiniMax M3—
Not directly comparable
VulcanBench v3
GPT-5.6 Sol87.0%
Source
MiniMax M3—
Not directly comparable
VulcanBench CII v1
GPT-5.6 Sol86.5%
Source
MiniMax M3—
Not directly comparable
LiveCodeBench (Vals)
GPT-5.6 Sol82.6%
Source
MiniMax M382.2%
Source
GPT-5.6 Sol leads this result
SWE-bench (Vals)
GPT-5.6 Sol96.2%
Source
MiniMax M375.0%
Source
GPT-5.6 Sol leads this result
cursorBench40
GPT-5.6 Sol41.7%
Source
MiniMax M3—
Not directly comparable
SWE-bench Verified
GPT-5.6 Sol—
MiniMax M380.5%
Source
Not directly comparable
NL2Repo
GPT-5.6 Sol—
MiniMax M342.1%
Source
Not directly comparable
VIBE V2
GPT-5.6 Sol—
MiniMax M350.1%
Source
Not directly comparable
SVG-Bench
GPT-5.6 Sol—
MiniMax M363.7%
Source
Not directly comparable
KernelBench Hard
GPT-5.6 Sol—
MiniMax M328.8%
Source
Not directly comparable
OpenHarmony Bench
GPT-5.6 Sol—
MiniMax M348.4%
Source
Not directly comparable

Reasoning

ARC-AGI-2
GPT-5.6 Sol92.5%
Source
MiniMax M3—
Not directly comparable
ARC-AGI-3
GPT-5.6 Sol7.8%
Source
MiniMax M3—
Not directly comparable
GeneBench-Pro
GPT-5.6 Sol28.7%
Source
MiniMax M3—
Not directly comparable

Multimodal

MMMU-Pro
GPT-5.6 Sol83%
Source
MiniMax M378.1%
Source
GPT-5.6 Sol leads this result
MMMU-Pro w/ Python
GPT-5.6 Sol84.6%
Source
MiniMax M3—
Not directly comparable
OfficeQA Pro
GPT-5.6 Sol—
MiniMax M345.1%
Source
Not directly comparable
OmniDocBench 1.5
GPT-5.6 Sol—
MiniMax M391.6%
Source
Not directly comparable
VideoMMMU
GPT-5.6 Sol—
MiniMax M384.6%
Source
Not directly comparable
Video-MME (with subtitle)
GPT-5.6 Sol—
MiniMax M385.4%
Source
Not directly comparable

Knowledge

GPQA
GPT-5.6 Sol94.6%
Source
MiniMax M3—
Not directly comparable
GPQA-D
GPT-5.6 Sol94.6%
Source
MiniMax M3—
Not directly comparable
HLE-Verified
GPT-5.6 Sol54.5%
Source
MiniMax M3—
Not directly comparable
LABBench2
GPT-5.6 Sol82.1%
Source
MiniMax M3—
Not directly comparable
HealthBench Professional
GPT-5.6 Sol60.5%
Source
MiniMax M3—
Not directly comparable
HealthBench Hard
GPT-5.6 Sol33.1%
Source
MiniMax M3—
Not directly comparable
GPQA Diamond (Vals)
GPT-5.6 Sol95.2%
Source
MiniMax M392.7%
Source
GPT-5.6 Sol leads this result
MMLU-Pro (Vals)
GPT-5.6 Sol89.1%
Source
MiniMax M384.2%
Source
GPT-5.6 Sol leads this result

Math

FrontierMath (legacy)
GPT-5.6 Sol89%
Source
MiniMax M3—
Not directly comparable
FrontierMath v2 (Tiers 1-3)
GPT-5.6 Sol89.000%
Source
MiniMax M3—
Not directly comparable
FrontierMath v2 (Tier 4)
GPT-5.6 Sol83.000%
Source
MiniMax M3—
Not directly comparable
USAMO 2026
GPT-5.6 Sol—
MiniMax M385.7%
Source
Not directly comparable

53 public results · 11 shared

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Last updated September 24, 2026

GPT-5.6 Sol vs MiniMax M3

Decision reading

Which one for your work

Coding work

Agentic work

Long documents

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Agentic

Coding

Knowledge

Multimodal

Instruction following

Reasoning

Multilingual

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 Sol or MiniMax M3?

Which is better for coding, GPT-5.6 Sol or MiniMax M3?

Which is better for agentic tasks, GPT-5.6 Sol or MiniMax M3?

Which costs less, GPT-5.6 Sol or MiniMax M3?

Which has the larger context window, GPT-5.6 Sol or MiniMax M3?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Math

Related comparisons

Watch GPT-5.6 Sol vs MiniMax M3