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Model comparison

GPT-4.1 mini vs MAI-Thinking-1

Updated August 7, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

GPT-4.1 mini

OpenAI

43.1/100

Estimated · Public rank #164

90% interval 31.6–54.6

MAI-Thinking-1

Microsoft

51.0/100

Estimated · Public rank #109

90% interval 41.2–60.9

MAI-Thinking-1 has the higher public score estimate, 51.03 versus 43.12, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

2 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-4.1 mini

    GPT-4.1 mini 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

    No shared weighted benchmark basis supports a winner.

    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: rate-fallback

  • 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
2
GPT-4.1 mini only
3
MAI-Thinking-1 only
12
Like-for-like categories
0 / 8

2 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.

Coding

Directional only
GPT-4.1 mini
23.6
MAI-Thinking-1
65.5
Weighted basis
1 vs 2 rows
Reading
Directional only

Knowledge

Directional only
GPT-4.1 mini
64.2
MAI-Thinking-1
72.5
Weighted basis
1 vs 3 rows
Reading
Directional only

Agentic

Not comparable
GPT-4.1 mini
Not measured
MAI-Thinking-1
46.0
Weighted basis
0 vs 1 rows
Reading
Not comparable

Reasoning

Not comparable
GPT-4.1 mini
Not measured
MAI-Thinking-1
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
GPT-4.1 mini
4.5
MAI-Thinking-1
89.7
Weighted basis
1 vs 2 rows
Reading
Not comparable

Multilingual

Not comparable
GPT-4.1 mini
Not measured
MAI-Thinking-1
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
GPT-4.1 mini
Not measured
MAI-Thinking-1
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
GPT-4.1 mini
88.5
MAI-Thinking-1
85.0
Weighted basis
1 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-4.1 mini
$0.0012
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-4.1 mini
$0.0248
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-4.1 mini
$0.104
Fits in one request
Cached input priced at the published list-input rate
MAI-Thinking-1
API rate not published
Fits in one request
Cached-input rate unavailable

GPT-4.1 mini has no published cached-input rate, so cached tokens use its listed input rate. 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-4.1 mini

1M

MAI-Thinking-1

256K

API model ID

GPT-4.1 mini

Not sourced

MAI-Thinking-1

Not sourced

Cached-input rate

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

GPT-4.1 mini

Not published

MAI-Thinking-1

No comparable hosted API rate

Documented inputs

GPT-4.1 mini

Not sourced

MAI-Thinking-1

Not sourced

Documented outputs

GPT-4.1 mini

Not sourced

MAI-Thinking-1

Not sourced

Provider availability

GPT-4.1 mini

Not sourced

MAI-Thinking-1

Not sourced

Reasoning profile

GPT-4.1 mini

Non-Reasoning

MAI-Thinking-1

Reasoning

Weight access

GPT-4.1 mini

Proprietary

MAI-Thinking-1

Proprietary

License

GPT-4.1 mini

Proprietary

MAI-Thinking-1

Proprietary

Release date

GPT-4.1 mini

2025-04-14

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
MAI-Thinking-1 has the higher public score estimate, 51.03 versus 43.12, but the 90% score intervals overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
GPT-4.1 mini has the larger documented window (1M).

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 evidence17 rows

Agentic

  • Terminal-Bench 2.0

    GPT-4.1 mini—
    MAI-Thinking-146%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    GPT-4.1 mini23.6%
    Source
    MAI-Thinking-173.5%
    Source

    MAI-Thinking-1 leads this result

  • LiveCodeBench v6

    GPT-4.1 mini—
    MAI-Thinking-187.7%
    Source

    Not directly comparable

  • SWE-bench Pro

    GPT-4.1 mini—
    MAI-Thinking-152.8%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    GPT-4.1 mini—
    MAI-Thinking-146.0%
    Source

    Not directly comparable

Reasoning

  • Graphwalks BFS 128K

    GPT-4.1 mini—
    MAI-Thinking-190%
    Source

    Not directly comparable

Knowledge

  • MMLU

    GPT-4.1 mini87.5%
    Source
    MAI-Thinking-1—

    Not directly comparable

  • GPQA

    GPT-4.1 mini64.2%
    Source
    MAI-Thinking-184.2%
    Source

    MAI-Thinking-1 leads this result

  • GPQA-D

    GPT-4.1 mini—
    MAI-Thinking-184.2%
    Source

    Not directly comparable

  • MMLU-Pro

    GPT-4.1 mini—
    MAI-Thinking-185%
    Source

    Not directly comparable

  • SimpleQA

    GPT-4.1 mini—
    MAI-Thinking-131%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    GPT-4.1 mini4.483%
    Source
    MAI-Thinking-1—

    Not directly comparable

  • AIME 2025

    GPT-4.1 mini—
    MAI-Thinking-197%
    Source

    Not directly comparable

  • AIME26

    GPT-4.1 mini—
    MAI-Thinking-194.5%
    Source

    Not directly comparable

  • HMMT Feb 2026

    GPT-4.1 mini—
    MAI-Thinking-184.9%
    Source

    Not directly comparable

Instruction following

  • IFEval

    GPT-4.1 mini88.5%
    Source
    MAI-Thinking-1—

    Not directly comparable

  • IFBench

    GPT-4.1 mini—
    MAI-Thinking-185%
    Source

    Not directly comparable

Frequently asked questions

Which is better, GPT-4.1 mini or MAI-Thinking-1?

MAI-Thinking-1 has the higher public score estimate, 51.03 versus 43.12, 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-4.1 mini 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-4.1 mini or MAI-Thinking-1?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, GPT-4.1 mini 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-4.1 mini or MAI-Thinking-1?

GPT-4.1 mini has the larger documented context window: 1M, compared with 256K.

Related comparisons

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Last updated August 7, 2026

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