Skip to main content
Radar

Every change to the models you run, with its source and its date. Releases, price changes, retirements, API changes, and incidents.Every change to the models you run, with its source.

Follow model changes
BenchLM

GPT-6 Sol vs MiniMax M3

Decision reading

GPT-6 Sol has the higher public score estimate, 80.45 versus 61.02, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

OpenAI logo
Model A
GPT-6 Sol

OpenAI

80.45/100

Estimated · Public rank #7

90% interval 51.1–92.0

MiniMax logo
Model B
MiniMax M3

MiniMax

61.02/100

Supported · Public rank #67

90% interval 51.2–70.9

Updated September 22, 2026. Rank says GPT-6 Sol is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Share or export

Share on XLinkedInSocial cardCSVJSON

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-6 Sol

    GPT-6 Sol has the larger documented context window.

    Confidence: documented

  • 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

Show secondary and unsupported calls

  • 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

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    GPT-6 Sol and MiniMax M3 are scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.

    Confidence: limited

  • Agentic work

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

    Not enough matched evidence

    GPT-6 Sol 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.

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

74.3GPT-6 Sol49.0MiniMax M3

Directional only · BenchAlign

GPT-6 Sol scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.

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

Open the coding leaderboardShortlist models for this job

What is actually comparable

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

Shared results
1
GPT-6 Sol only
9
MiniMax M3 only
26
Like-for-like categories
0 / 8

3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign 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

Directional only
GPT-6 Sol
69.7
Estimated · #7/157
MiniMax M3
42.3
Supported · #112/157
Basis
BenchAlign lane · 4 vs 9 public rows
Reading
Directional only

Coding

Directional only
GPT-6 Sol
74.3
Estimated · #6/159
MiniMax M3
49.0
Estimated · #74/159
Basis
BenchAlign lane · 1 vs 10 public rows
Reading
Directional only

Knowledge

Directional only
GPT-6 Sol
80.2
Estimated · #6/189
MiniMax M3
52.3
Supported · #72/189
Basis
BenchAlign lane · 5 vs 2 public rows
Reading
Directional only

Reasoning

Not comparable
GPT-6 Sol
78.5
#5/17
MiniMax M3
78.0
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
GPT-6 Sol
82.8
Unranked · 1 rankable row
MiniMax M3
52.0
#36/50
Basis
Provisional lane · 0 vs 2 weighted rows
Reading
Not comparable

Multilingual

Not comparable
GPT-6 Sol
Not ranked
MiniMax M3
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
GPT-6 Sol
Not ranked
MiniMax M3
92.4
#5/124
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
GPT-6 Sol
Not ranked
MiniMax M3
Not ranked
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

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

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.

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 Sol
$0.007
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-6 Sol
$0.13
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-6 Sol
$0.18
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.

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 Sol

$0.2 per 1M cached input tokens

OpenAI GPT-6 Sol model documentation

MiniMax M3

$0.06 per 1M cached input tokens

Provider availability

GPT-6 Sol

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

OpenAI GPT-6 Sol and Luna launch

MiniMax M3

Not sourced

Reasoning profile

GPT-6 Sol

Reasoning

MiniMax M3

Non-Reasoning

Weight access

GPT-6 Sol

Proprietary

MiniMax M3

Open Weight

License

GPT-6 Sol

Proprietary

MiniMax M3

Open Weight

Release date

GPT-6 Sol

2026-09-16

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-6 Sol has the higher public score estimate, 80.45 versus 61.02, but the 90% score intervals overlap.
Workload cost
Repository review: $0.13 vs $0.0186. Cache-heavy agent loop: $0.18 vs $0.03.
Context tradeoff
GPT-6 Sol has the larger documented window (1.05M).

Run the same representative tasks against both endpoints before changing production traffic.

Benchmark evidence

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

Browse raw public benchmark evidence36 rows

Agentic

  • Agents' Last Exam

    GPT-6 Sol56.4%
    Source
    MiniMax M3—

    Not directly comparable

  • AutomationBench

    GPT-6 Sol33.2%
    Source
    MiniMax M3—

    Not directly comparable

  • OSWorld 2.0

    GPT-6 Sol60.5%
    Source
    MiniMax M34.6%
    Source

    GPT-6 Sol leads this result

  • ExploitGym

    GPT-6 Sol22.1%
    Source
    MiniMax M3—

    Not directly comparable

  • Terminal-Bench 2.0

    GPT-6 Sol—
    MiniMax M366%
    Source

    Not directly comparable

  • BrowseComp

    GPT-6 Sol—
    MiniMax M383.5%
    Source

    Not directly comparable

  • OSWorld-Verified

    GPT-6 Sol—
    MiniMax M370.1%
    Source

    Not directly comparable

  • MCP Atlas

    GPT-6 Sol—
    MiniMax M374.2%
    Source

    Not directly comparable

  • Claw-Eval

    GPT-6 Sol—
    MiniMax M374.5%
    Source

    Not directly comparable

  • BankerToolBench

    GPT-6 Sol—
    MiniMax M376.1%
    Source

    Not directly comparable

  • ResearchClawBench

    GPT-6 Sol—
    MiniMax M319.8%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    GPT-6 Sol—
    MiniMax M353.6%
    Source

    Not directly comparable

Coding

  • DeepSWE

    GPT-6 Sol68.8%
    Source
    MiniMax M3—

    Not directly comparable

  • SWE-bench Verified

    GPT-6 Sol—
    MiniMax M380.5%
    Source

    Not directly comparable

  • SWE-bench Pro

    GPT-6 Sol—
    MiniMax M359%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    GPT-6 Sol—
    MiniMax M366.0%
    Source

    Not directly comparable

  • NL2Repo

    GPT-6 Sol—
    MiniMax M342.1%
    Source

    Not directly comparable

  • VIBE V2

    GPT-6 Sol—
    MiniMax M350.1%
    Source

    Not directly comparable

  • SVG-Bench

    GPT-6 Sol—
    MiniMax M363.7%
    Source

    Not directly comparable

  • KernelBench Hard

    GPT-6 Sol—
    MiniMax M328.8%
    Source

    Not directly comparable

  • OpenHarmony Bench

    GPT-6 Sol—
    MiniMax M348.4%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    GPT-6 Sol—
    MiniMax M382.2%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    GPT-6 Sol—
    MiniMax M375.0%
    Source

    Not directly comparable

Multimodal

  • OfficeQA Pro

    GPT-6 Sol—
    MiniMax M345.1%
    Source

    Not directly comparable

  • OmniDocBench 1.5

    GPT-6 Sol—
    MiniMax M391.6%
    Source

    Not directly comparable

  • MMMU-Pro

    GPT-6 Sol—
    MiniMax M378.1%
    Source

    Not directly comparable

  • VideoMMMU

    GPT-6 Sol—
    MiniMax M384.6%
    Source

    Not directly comparable

  • Video-MME (with subtitle)

    GPT-6 Sol—
    MiniMax M385.4%
    Source

    Not directly comparable

Knowledge

  • HealthBench (raw)

    GPT-6 Sol47.1%
    Source
    MiniMax M3—

    Not directly comparable

  • HealthBench (length-adjusted)

    GPT-6 Sol53.2%
    Source
    MiniMax M3—

    Not directly comparable

  • HealthBench Professional

    GPT-6 Sol60.8%
    Source
    MiniMax M3—

    Not directly comparable

  • HealthBench Professional (raw)

    GPT-6 Sol59.5%
    Source
    MiniMax M3—

    Not directly comparable

  • HealthBench Hard

    GPT-6 Sol30.1%
    Source
    MiniMax M3—

    Not directly comparable

  • GPQA Diamond (Vals)

    GPT-6 Sol—
    MiniMax M392.7%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    GPT-6 Sol—
    MiniMax M384.2%
    Source

    Not directly comparable

Math

  • USAMO 2026

    GPT-6 Sol—
    MiniMax M385.7%
    Source

    Not directly comparable

Questions

Which is better, GPT-6 Sol or MiniMax M3?

GPT-6 Sol has the higher public score estimate, 80.45 versus 61.02, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, GPT-6 Sol or MiniMax M3?

GPT-6 Sol scores higher for coding on the public lane, 74.3 to 49. GPT-6 Sol and MiniMax M3 are scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, GPT-6 Sol or MiniMax M3?

GPT-6 Sol scores higher for agentic tasks on the public lane, 69.7 to 42.3. GPT-6 Sol 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 Sol or MiniMax M3?

For the stated presets, chat costs $0.007 on GPT-6 Sol and $0.0009 on MiniMax M3; repository review costs $0.13 and $0.0186; the cache-heavy agent loop costs $0.18 and $0.03. Costs use the listed standard API rates.

Which has the larger context window, GPT-6 Sol or MiniMax M3?

GPT-6 Sol has the larger documented context window: 1.05M, compared with 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 22, 2026

Watch GPT-6 Sol vs MiniMax M3

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

Read a sample issue

Join 2,000+ readers.