Skip to main content
Radar

Provider changes are easy to miss. Radar watches releases, pricing, deprecations, and incidents at the source.Provider changes are easy to miss.

See Radar
BenchLM
Advertise

Model comparison

GPT-5.6 Sol vs MAI-Thinking-1

Updated August 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

GPT-5.6 Sol

OpenAI

81.5/100

Supported · Public rank #4

90% interval 77.7–85.3

MAI-Thinking-1

Microsoft

51.0/100

Estimated · Public rank #109

90% interval 41.2–60.9

GPT-5.6 Sol has the higher public score, 81.48 versus 51.05, and the 90% score intervals do not overlap.

5 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

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 Sol

    GPT-5.6 Sol has the larger documented context window.

    Confidence: documented

Show secondary and unsupported calls

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.

    Confidence: limited

  • Agentic work

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

    Not enough matched evidence

    The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.

    Confidence: limited

  • Chat turn cost

    1K fresh input + 500 output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

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

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • Repository review cost

    50K fresh input + 3K output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

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
5
GPT-5.6 Sol only
19
MAI-Thinking-1 only
9
Like-for-like categories
0 / 8

3 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Agentic

Directional only
GPT-5.6 Sol
92.0
MAI-Thinking-1
46.0
Weighted basis
2 vs 1 rows
Reading
Directional only

Coding

Directional only
GPT-5.6 Sol
64.6
MAI-Thinking-1
65.5
Weighted basis
1 vs 2 rows
Reading
Directional only

Knowledge

Directional only
GPT-5.6 Sol
94.6
MAI-Thinking-1
72.5
Weighted basis
1 vs 3 rows
Reading
Directional only

Reasoning

Not comparable
GPT-5.6 Sol
92.5
MAI-Thinking-1
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Math

Not comparable
GPT-5.6 Sol
87.5
MAI-Thinking-1
89.7
Weighted basis
2 vs 2 rows
Reading
Not comparable

Multilingual

Not comparable
GPT-5.6 Sol
Not measured
MAI-Thinking-1
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
GPT-5.6 Sol
83.0
MAI-Thinking-1
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
GPT-5.6 Sol
Not measured
MAI-Thinking-1
85.0
Weighted basis
0 vs 1 rows
Reading
Not comparable

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-5.6 Sol
$0.02
Fits in one request
MAI-Thinking-1
API rate not published
Fits in one request

MAI-Thinking-1 has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GPT-5.6 Sol
$0.34
Fits in one request
MAI-Thinking-1
API rate not published
Fits in one request

MAI-Thinking-1 has no comparable published API token rate.

Cache-heavy agent loop

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

GPT-5.6 Sol
$0.5
Fits in one request
MAI-Thinking-1
API rate not published
Fits in one request
Cached-input rate unavailable

MAI-Thinking-1 has no comparable published API token rate.

Specification differences

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

Context window

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

GPT-5.6 Sol

1.05M

OpenAI model catalog

MAI-Thinking-1

256K

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

$0.5 per 1M cached input tokens

OpenAI pricing

MAI-Thinking-1

No comparable hosted API rate

Provider availability

GPT-5.6 Sol

Generally Available · OpenAI Responses API

OpenAI model catalog

MAI-Thinking-1

Not sourced

Reasoning profile

GPT-5.6 Sol

Reasoning

MAI-Thinking-1

Reasoning

Weight access

GPT-5.6 Sol

Proprietary

MAI-Thinking-1

Proprietary

License

GPT-5.6 Sol

Proprietary

MAI-Thinking-1

Proprietary

Release date

GPT-5.6 Sol

2026-07-09

MAI-Thinking-1

2026-06-02

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, 81.48 versus 51.05, and the 90% score intervals do not overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
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.

Benchmark evidence

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

Browse raw public benchmark evidence33 rows

Agentic

  • Terminal-Bench 2.0

    GPT-5.6 Sol91.9%
    Source
    MAI-Thinking-146%
    Source

    GPT-5.6 Sol leads this result

  • BrowseComp

    GPT-5.6 Sol92.2%
    Source
    MAI-Thinking-1—

    Not directly comparable

  • OSWorld 2.0

    GPT-5.6 Sol62.6%
    Source
    MAI-Thinking-1—

    Not directly comparable

  • CyberGym

    GPT-5.6 Sol84.5%
    Source
    MAI-Thinking-1—

    Not directly comparable

  • ExploitGym

    GPT-5.6 Sol33.7%
    Source
    MAI-Thinking-1—

    Not directly comparable

  • Toolathlon

    GPT-5.6 Sol58%
    Source
    MAI-Thinking-1—

    Not directly comparable

Coding

  • SWE-bench Pro

    GPT-5.6 Sol64.6%
    Source
    MAI-Thinking-152.8%
    Source

    GPT-5.6 Sol leads this result

  • Terminal-Bench 2.0

    GPT-5.6 Sol91.9%
    Source
    MAI-Thinking-146.0%
    Source

    GPT-5.6 Sol leads this result

  • deepSwe

    GPT-5.6 Sol72.7%
    Source
    MAI-Thinking-1—

    Not directly comparable

  • FrontierCode 1.1 Extended

    GPT-5.6 Sol60.6%
    Source
    MAI-Thinking-1—

    Not directly comparable

  • cursorBench32

    GPT-5.6 Sol67.2%
    Source
    MAI-Thinking-1—

    Not directly comparable

  • VulcanBench v3

    GPT-5.6 Sol87.0%
    Source
    MAI-Thinking-1—

    Not directly comparable

  • LiveCodeBench v6

    GPT-5.6 Sol—
    MAI-Thinking-187.7%
    Source

    Not directly comparable

  • SWE-bench Verified

    GPT-5.6 Sol—
    MAI-Thinking-173.5%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    GPT-5.6 Sol92.5%
    Source
    MAI-Thinking-1—

    Not directly comparable

  • ARC-AGI-3

    GPT-5.6 Sol7.8%
    Source
    MAI-Thinking-1—

    Not directly comparable

  • GeneBench-Pro

    GPT-5.6 Sol28.7%
    Source
    MAI-Thinking-1—

    Not directly comparable

  • Graphwalks BFS 128K

    GPT-5.6 Sol—
    MAI-Thinking-190%
    Source

    Not directly comparable

Knowledge

  • GPQA

    GPT-5.6 Sol94.6%
    Source
    MAI-Thinking-184.2%
    Source

    GPT-5.6 Sol leads this result

  • GPQA-D

    GPT-5.6 Sol94.6%
    Source
    MAI-Thinking-184.2%
    Source

    GPT-5.6 Sol leads this result

  • HealthBench Professional

    GPT-5.6 Sol60.5%
    Source
    MAI-Thinking-1—

    Not directly comparable

  • HealthBench Hard

    GPT-5.6 Sol33.1%
    Source
    MAI-Thinking-1—

    Not directly comparable

  • MMLU-Pro

    GPT-5.6 Sol—
    MAI-Thinking-185%
    Source

    Not directly comparable

  • SimpleQA

    GPT-5.6 Sol—
    MAI-Thinking-131%
    Source

    Not directly comparable

Math

  • FrontierMath (legacy)

    GPT-5.6 Sol89%
    Source
    MAI-Thinking-1—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    GPT-5.6 Sol89.000%
    Source
    MAI-Thinking-1—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    GPT-5.6 Sol83.000%
    Source
    MAI-Thinking-1—

    Not directly comparable

  • AIME 2025

    GPT-5.6 Sol—
    MAI-Thinking-197%
    Source

    Not directly comparable

  • AIME26

    GPT-5.6 Sol—
    MAI-Thinking-194.5%
    Source

    Not directly comparable

  • HMMT Feb 2026

    GPT-5.6 Sol—
    MAI-Thinking-184.9%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    GPT-5.6 Sol83%
    Source
    MAI-Thinking-1—

    Not directly comparable

  • MMMU-Pro w/ Python

    GPT-5.6 Sol84.6%
    Source
    MAI-Thinking-1—

    Not directly comparable

Instruction following

  • IFBench

    GPT-5.6 Sol—
    MAI-Thinking-185%
    Source

    Not directly comparable

Frequently asked questions

Which is better, GPT-5.6 Sol or MAI-Thinking-1?

GPT-5.6 Sol has the higher public score, 81.48 versus 51.05, 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 MAI-Thinking-1?

The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, GPT-5.6 Sol or MAI-Thinking-1?

The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

Which costs less, GPT-5.6 Sol or MAI-Thinking-1?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, GPT-5.6 Sol or MAI-Thinking-1?

GPT-5.6 Sol has the larger documented context window: 1.05M, compared with 256K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 10, 2026

Watch GPT-5.6 Sol vs MAI-Thinking-1

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

Read a sample issue

Join 2,000+ readers.