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Thinking Machines Lab logo
Model A
Inkling

Thinking Machines Lab

67.02/100

Supported · Public rank #31

90% interval 60.0–74.0

Inkling vs o1-pro

Updated August 29, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

OpenAI logo
Model B
o1-pro

OpenAI

46.25/100

Estimated · Public rank #163

90% interval 34.7–57.8

Decision reading

Inkling has the higher public score, 67.02 versus 46.25, and the 90% score intervals do not overlap.

1 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

    Inkling

    Inkling has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    Inkling

    Inkling 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

    Inkling

    Inkling 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

    No shared weighted benchmark basis supports a winner.

    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

  • Cache-heavy agent loop cost

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

    Not enough matched evidence

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. o1-pro does not fit this workload in one request. o1-pro has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

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
Inkling only
14
o1-pro only
0
Like-for-like categories
0 / 8

1 category uses 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.

Knowledge

Directional only
Inkling
51.6
o1-pro
79.0
Weighted basis
2 vs 1 rows
Reading
Directional only

Agentic

Not comparable
Inkling
69.4
o1-pro
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Coding

Not comparable
Inkling
68.6
o1-pro
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
Inkling
Not measured
o1-pro
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
Inkling
97.1
o1-pro
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Inkling
Not measured
o1-pro
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Inkling
76.5
o1-pro
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Inkling
79.8
o1-pro
Not measured
Weighted basis
1 vs 0 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

Inkling
$0.00421
Fits in one request
o1-pro
$0.45
Fits in one request

Inkling has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Inkling
$0.10754
Fits in one request
o1-pro
$9.30
Fits in one request

Inkling has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

Inkling
$0.159
Fits in one request
o1-pro
$39.00
Does not fit in one request
Cached input priced at the published list-input rate

o1-pro does not fit this workload in one request. o1-pro has no published cached-input rate, so cached tokens use its listed input 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.

Inkling

1M

o1-pro

200K

API model ID

Inkling

Not sourced

o1-pro

Not sourced

Cached-input rate

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

Inkling

$0.374 per 1M cached input tokens

o1-pro

Not published

Documented inputs

Inkling

Not sourced

o1-pro

Not sourced

Documented outputs

Inkling

Not sourced

o1-pro

Not sourced

Provider availability

Inkling

Not sourced

o1-pro

Not sourced

Reasoning profile

Inkling

Hybrid

o1-pro

Reasoning

Weight access

Inkling

Open Weight

o1-pro

Proprietary

License

Inkling

Open Weight

o1-pro

Proprietary

Release date

Inkling

2026-07-15

o1-pro

2024-12-01

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
Inkling has the higher public score, 67.02 versus 46.25, and the 90% score intervals do not overlap.
Workload cost
Repository review: $0.10754 vs $9.30. Cache-heavy agent loop: $0.159 vs $39.00.
Context tradeoff
Inkling 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 evidence15 rows

Agentic

  • Terminal-Bench 2.0

    Inkling63.8%
    Source
    o1-pro

    Not directly comparable

  • BrowseComp

    Inkling77.1%
    Source
    o1-pro

    Not directly comparable

  • MCP Atlas

    Inkling74.1%
    Source
    o1-pro

    Not directly comparable

Coding

  • SWE-bench Verified

    Inkling77.6%
    Source
    o1-pro

    Not directly comparable

  • SWE-bench Pro

    Inkling54.3%
    Source
    o1-pro

    Not directly comparable

  • Terminal-Bench 2.0

    Inkling63.8%
    Source
    o1-pro

    Not directly comparable

Knowledge

  • GPQA

    Inkling87.9%
    Source
    o1-pro79%
    Source

    Inkling leads this result

  • GPQA-D

    Inkling87.9%
    Source
    o1-pro

    Not directly comparable

  • HLE

    Inkling46%
    Source
    o1-pro

    Not directly comparable

  • HLE w/o tools

    Inkling30%
    Source
    o1-pro

    Not directly comparable

Math

  • AIME26

    Inkling97.1%
    Source
    o1-pro

    Not directly comparable

Multimodal

  • MMMU-Pro

    Inkling73.5%
    Source
    o1-pro

    Not directly comparable

  • CharXiv

    Inkling82%
    Source
    o1-pro

    Not directly comparable

  • CharXiv w/o tools

    Inkling78.1%
    Source
    o1-pro

    Not directly comparable

Instruction following

  • IFBench

    Inkling79.8%
    Source
    o1-pro

    Not directly comparable

Frequently asked questions

Which is better, Inkling or o1-pro?

Inkling has the higher public score, 67.02 versus 46.25, 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, Inkling or o1-pro?

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

Which is better for agentic tasks, Inkling or o1-pro?

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, Inkling or o1-pro?

For the stated presets, chat costs $0.00421 on Inkling and $0.45 on o1-pro; repository review costs $0.10754 and $9.30; the cache-heavy agent loop costs $0.159 and $39.00. o1-pro does not fit this workload in one request. o1-pro has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Inkling or o1-pro?

Inkling has the larger documented context window: 1M, compared with 200K.

Related comparisons

Last updated August 29, 2026

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