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BenchLM
Microsoft logo
Model A
MAI-Thinking-1

Microsoft

52.27/100

Estimated · Public rank #123

90% interval 42.4–62.1

MAI-Thinking-1 vs Nemotron 3 Nano Omni 30B A3B

Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

NVIDIA logo
Model B
Nemotron 3 Nano Omni 30B A3B

NVIDIA

43.97/100

Estimated · Public rank #172

90% interval 32.5–55.5

Decision reading

MAI-Thinking-1 has the higher public score estimate, 52.27 versus 43.97, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

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

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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.

No workload recommendation clears the current evidence threshold.

Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.

Show secondary and unsupported calls

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    MAI-Thinking-1 and Nemotron 3 Nano Omni 30B A3B 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

    MAI-Thinking-1 and Nemotron 3 Nano Omni 30B A3B are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented

  • 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
MAI-Thinking-1 only
9
Nemotron 3 Nano Omni 30B A3B only
11
Like-for-like categories
1 / 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.

Instruction following

Like-for-like
MAI-Thinking-1
94.7
#1/120
Nemotron 3 Nano Omni 30B A3B
75.9
#57/120
Basis
Provisional lane · 1 vs 1 weighted rows
Reading
MAI-Thinking-1 leads

Agentic

Directional only
MAI-Thinking-1
51.7
Estimated · #53/151
Nemotron 3 Nano Omni 30B A3B
47.0
Estimated · #83/151
Basis
BenchAlign lane · 1 vs 2 public rows
Reading
Directional only

Coding

Directional only
MAI-Thinking-1
51.8
Estimated · #60/183
Nemotron 3 Nano Omni 30B A3B
42.6
Estimated · #129/183
Basis
BenchAlign lane · 4 vs 2 public rows
Reading
Directional only

Knowledge

Directional only
MAI-Thinking-1
53.3
Estimated · #68/181
Nemotron 3 Nano Omni 30B A3B
39.9
Supported · #140/181
Basis
BenchAlign lane · 4 vs 3 public rows
Reading
Directional only

Reasoning

Not comparable
MAI-Thinking-1
Not ranked
Nemotron 3 Nano Omni 30B A3B
50.3
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
MAI-Thinking-1
73.4
Unranked · 3 rankable rows
Nemotron 3 Nano Omni 30B A3B
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
MAI-Thinking-1
Not ranked
Nemotron 3 Nano Omni 30B A3B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
MAI-Thinking-1
Not ranked
Nemotron 3 Nano Omni 30B A3B
39.3
#42/48
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

MAI-Thinking-1
API rate not published
Fits in one request
Nemotron 3 Nano Omni 30B A3B
Self-hosted; infrastructure cost varies
Fits in one request

MAI-Thinking-1 has no comparable published API token rate. Nemotron 3 Nano Omni 30B A3B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

MAI-Thinking-1
API rate not published
Fits in one request
Nemotron 3 Nano Omni 30B A3B
Self-hosted; infrastructure cost varies
Fits in one request

MAI-Thinking-1 has no comparable published API token rate. Nemotron 3 Nano Omni 30B A3B has no comparable published API token rate.

Cache-heavy agent loop

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

MAI-Thinking-1
API rate not published
Fits in one request
Cached-input rate unavailable
Nemotron 3 Nano Omni 30B A3B
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

MAI-Thinking-1 has no comparable published API token rate. Nemotron 3 Nano Omni 30B A3B 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.

MAI-Thinking-1

256K

Nemotron 3 Nano Omni 30B A3B

256K

API model ID

MAI-Thinking-1

Not sourced

Nemotron 3 Nano Omni 30B A3B

Not sourced

Cached-input rate

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

MAI-Thinking-1

No comparable hosted API rate

Nemotron 3 Nano Omni 30B A3B

No comparable hosted API rate

Documented inputs

MAI-Thinking-1

Not sourced

Nemotron 3 Nano Omni 30B A3B

Not sourced

Documented outputs

MAI-Thinking-1

Not sourced

Nemotron 3 Nano Omni 30B A3B

Not sourced

Provider availability

MAI-Thinking-1

Not sourced

Nemotron 3 Nano Omni 30B A3B

Not sourced

Reasoning profile

MAI-Thinking-1

Reasoning

Nemotron 3 Nano Omni 30B A3B

Reasoning

Weight access

MAI-Thinking-1

Proprietary

Nemotron 3 Nano Omni 30B A3B

Open Weight

License

MAI-Thinking-1

Proprietary

Nemotron 3 Nano Omni 30B A3B

Open Weight

Release date

MAI-Thinking-1

2026-06-02

Nemotron 3 Nano Omni 30B A3B

2026-04-28

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, 52.27 versus 43.97, but the 90% score intervals overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Both models list 256K.

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 evidence25 rows

Agentic

  • Terminal-Bench 2.0

    MAI-Thinking-146%
    Source
    Nemotron 3 Nano Omni 30B A3B—

    Not directly comparable

  • OSWorld

    MAI-Thinking-1—
    Nemotron 3 Nano Omni 30B A3B47.4%
    Source

    Not directly comparable

  • τ²-bench results

    MAI-Thinking-1—
    Nemotron 3 Nano Omni 30B A3B45.3%
    Source

    Not directly comparable

Coding

  • LiveCodeBench v6

    MAI-Thinking-187.7%
    Source
    Nemotron 3 Nano Omni 30B A3B—

    Not directly comparable

  • SWE-bench Verified

    MAI-Thinking-173.5%
    Source
    Nemotron 3 Nano Omni 30B A3B—

    Not directly comparable

  • SWE-bench Pro

    MAI-Thinking-152.8%
    Source
    Nemotron 3 Nano Omni 30B A3B—

    Not directly comparable

  • Terminal-Bench 2.0

    MAI-Thinking-146.0%
    Source
    Nemotron 3 Nano Omni 30B A3B—

    Not directly comparable

  • LiveCodeBench v5

    MAI-Thinking-1—
    Nemotron 3 Nano Omni 30B A3B63.2%
    Source

    Not directly comparable

  • SciCode

    MAI-Thinking-1—
    Nemotron 3 Nano Omni 30B A3B32%
    Source

    Not directly comparable

Reasoning

  • Graphwalks BFS 128K

    MAI-Thinking-190%
    Source
    Nemotron 3 Nano Omni 30B A3B—

    Not directly comparable

Knowledge

  • GPQA

    MAI-Thinking-184.2%
    Source
    Nemotron 3 Nano Omni 30B A3B72.2%
    Source

    MAI-Thinking-1 leads this result

  • GPQA-D

    MAI-Thinking-184.2%
    Source
    Nemotron 3 Nano Omni 30B A3B72.2%
    Source

    MAI-Thinking-1 leads this result

  • MMLU-Pro

    MAI-Thinking-185%
    Source
    Nemotron 3 Nano Omni 30B A3B77.3%
    Source

    MAI-Thinking-1 leads this result

  • SimpleQA

    MAI-Thinking-131%
    Source
    Nemotron 3 Nano Omni 30B A3B—

    Not directly comparable

Math

  • AIME 2025

    MAI-Thinking-197%
    Source
    Nemotron 3 Nano Omni 30B A3B82.1%
    Source

    MAI-Thinking-1 leads this result

  • AIME26

    MAI-Thinking-194.5%
    Source
    Nemotron 3 Nano Omni 30B A3B—

    Not directly comparable

  • HMMT Feb 2026

    MAI-Thinking-184.9%
    Source
    Nemotron 3 Nano Omni 30B A3B—

    Not directly comparable

Multimodal

  • MMMU

    MAI-Thinking-1—
    Nemotron 3 Nano Omni 30B A3B70.8%
    Source

    Not directly comparable

  • MMLongBench-Doc

    MAI-Thinking-1—
    Nemotron 3 Nano Omni 30B A3B57.5%
    Source

    Not directly comparable

  • CharXiv

    MAI-Thinking-1—
    Nemotron 3 Nano Omni 30B A3B76.3%
    Source

    Not directly comparable

  • ScreenSpot Pro

    MAI-Thinking-1—
    Nemotron 3 Nano Omni 30B A3B57.8%
    Source

    Not directly comparable

  • Video-MME (w/o subtitle)

    MAI-Thinking-1—
    Nemotron 3 Nano Omni 30B A3B72.2%
    Source

    Not directly comparable

  • AI2D_TEST

    MAI-Thinking-1—
    Nemotron 3 Nano Omni 30B A3B88.5%
    Source

    Not directly comparable

  • RefCOCO (avg)

    MAI-Thinking-1—
    Nemotron 3 Nano Omni 30B A3B90.5%
    Source

    Not directly comparable

Instruction following

  • IFBench

    MAI-Thinking-185%
    Source
    Nemotron 3 Nano Omni 30B A3B74.2%
    Source

    MAI-Thinking-1 leads this result

Frequently asked questions

Which is better, MAI-Thinking-1 or Nemotron 3 Nano Omni 30B A3B?

MAI-Thinking-1 has the higher public score estimate, 52.27 versus 43.97, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, MAI-Thinking-1 or Nemotron 3 Nano Omni 30B A3B?

MAI-Thinking-1 scores higher for coding on the public lane, 51.8 to 42.6. MAI-Thinking-1 and Nemotron 3 Nano Omni 30B A3B 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, MAI-Thinking-1 or Nemotron 3 Nano Omni 30B A3B?

MAI-Thinking-1 scores higher for agentic tasks on the public lane, 51.7 to 47. MAI-Thinking-1 and Nemotron 3 Nano Omni 30B A3B are 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, MAI-Thinking-1 or Nemotron 3 Nano Omni 30B A3B?

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, MAI-Thinking-1 or Nemotron 3 Nano Omni 30B A3B?

Both models list the same context window, 256K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 4, 2026

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