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Inkling vs o4-mini

Updated October 14, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

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Decision reading

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Thinking Machines Lab logo

Thinking Machines Lab

52.13/100

Supported · Public rank #85

90% interval 41.7–62.5

Model B
OpenAI logo

OpenAI

—

Evidence status unavailable

90% interval unavailable

Shared results
1
Inkling only
23
o4-mini only
6
Like-for-like categories
0 / 8
Supported: InklingHow the comparison works

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

    o4-mini

    o4-mini 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

    o4-mini

    o4-mini 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

    O4-mini is not ranked on the public lane for coding, so no winner is named for coding.

    Confidence: limited
  • Agentic work

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

    Not enough matched evidence

    O4-mini is not ranked on the public lane for agentic, so no winner is named for agentic.

    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. o4-mini does not fit this workload in one request. o4-mini has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

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.

34.0Inkling—o4-mini

Not comparable · BenchAlign v5.8

The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.

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

Same basis rules as the category table below

What is actually comparable

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

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.

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 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

Not comparable
Inkling
36.2
Supported · #72/124
o4-mini
Not ranked
Basis
BenchAlign v5.8 lane · 5 vs 0 public rows
Reading
Not comparable

Coding

Not comparable
Inkling
34.0
Supported · #80/149
o4-mini
Not ranked
Basis
BenchAlign v5.8 lane · 6 vs 1 public rows
Reading
Not comparable

Reasoning

Not comparable
Inkling
79.0
#14/29
o4-mini
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Inkling
67.5
#35/54
o4-mini
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Inkling
54.5
Supported · #62/181
o4-mini
Not ranked
Basis
BenchAlign v5.8 lane · 6 vs 3 public rows
Reading
Not comparable

Multilingual

Not comparable
Inkling
Not ranked
o4-mini
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Inkling
87.6
#35/128
o4-mini
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Inkling
58.3
#11/12
o4-mini
Not ranked
Basis
Provisional lane · 3 vs 0 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

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
o4-mini
$0.0033
Fits in one request

o4-mini 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
o4-mini
$0.0682
Fits in one request

o4-mini 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
o4-mini
$0.286
Does not fit in one request
Cached input priced at the published list-input rate

o4-mini does not fit this workload in one request. o4-mini has no published cached-input rate, so cached tokens use its listed input rate.

Cached input falls back to the list input rate only where a cached rate is unpublished

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

o4-mini

200K

API model ID

Inkling

Not sourced

o4-mini

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

o4-mini

Not published

Documented inputs

Inkling

Not sourced

o4-mini

Not sourced

Documented outputs

Inkling

Not sourced

o4-mini

Not sourced

Provider availability

Inkling

Not sourced

o4-mini

Not sourced

Reasoning profile

Inkling

Hybrid

o4-mini

Reasoning

Weight access

Inkling

Open Weight

o4-mini

Proprietary

License

Inkling

Open Weight

o4-mini

Proprietary

Release date

Inkling

2026-07-15

o4-mini

2025-04-16

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
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
Workload cost
Repository review: $0.10754 vs $0.0682. Cache-heavy agent loop: $0.159 vs $0.286.
Context tradeoff
Inkling has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Inkling or o4-mini?

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Inkling or o4-mini?

O4-mini is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Inkling or o4-mini?

O4-mini is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Inkling or o4-mini?

For the stated presets, chat costs $0.00421 on Inkling and $0.0033 on o4-mini; repository review costs $0.10754 and $0.0682; the cache-heavy agent loop costs $0.159 and $0.286. o4-mini does not fit this workload in one request. o4-mini has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Inkling or o4-mini?

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

Benchmark evidence

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

Browse raw public benchmark evidence30 rows

Agentic

  • Terminal-Bench 2.1

    Inkling63.8%
    Source
    o4-mini—

    Not directly comparable

  • BrowseComp

    Inkling77.1%
    Source
    o4-mini—

    Not directly comparable

  • MCP Atlas

    Inkling74.1%
    Source
    o4-mini—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Inkling47.6%
    Source
    o4-mini—

    Not directly comparable

  • CWE-bench v1

    Inkling37.0%
    Source
    o4-mini—

    Not directly comparable

Coding

  • SWE-bench Verified

    Inkling77.6%
    Source
    o4-mini—

    Not directly comparable

  • SWE-bench Pro

    Inkling54.3%
    Source
    o4-mini—

    Not directly comparable

  • Terminal-Bench 2.1

    Inkling63.8%
    Source
    o4-mini—

    Not directly comparable

  • FrontierSWE v2

    Inkling4.1%
    Source
    o4-mini—

    Not directly comparable

  • LiveCodeBench (Vals)

    Inkling85.5%
    Source
    o4-mini—

    Not directly comparable

  • SWE-bench (Vals)

    Inkling77.6%
    Source
    o4-mini—

    Not directly comparable

  • HumanEval

    Inkling—
    o4-mini97.3%
    Source

    Not directly comparable

Reasoning

  • DROP

    Inkling—
    o4-mini77.7%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    Inkling73.5%
    Source
    o4-mini—

    Not directly comparable

  • CharXiv

    Inkling82%
    Source
    o4-mini—

    Not directly comparable

  • CharXiv w/o tools

    Inkling78.1%
    Source
    o4-mini—

    Not directly comparable

Knowledge

  • GPQA

    Inkling87.9%
    Source
    o4-mini77.6%
    Source

    Inkling leads this result

  • GPQA-D

    Inkling87.9%
    Source
    o4-mini—

    Not directly comparable

  • HLE

    Inkling46%
    Source
    o4-mini—

    Not directly comparable

  • HLE w/o tools

    Inkling30%
    Source
    o4-mini—

    Not directly comparable

  • GPQA Diamond (Vals)

    Inkling87.1%
    Source
    o4-mini—

    Not directly comparable

  • MMLU-Pro (Vals)

    Inkling86.3%
    Source
    o4-mini—

    Not directly comparable

  • MMLU

    Inkling—
    o4-mini90%
    Source

    Not directly comparable

  • SimpleQA

    Inkling—
    o4-mini20.2%
    Source

    Not directly comparable

Multilingual

  • MGSM

    Inkling—
    o4-mini93.7%
    Source

    Not directly comparable

Instruction following

  • IFBench

    Inkling79.8%
    Source
    o4-mini—

    Not directly comparable

Math

  • AIME26

    Inkling97.1%
    Source
    o4-mini—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    Inkling33.333%
    Source
    o4-mini—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Inkling4.878%
    Source
    o4-mini—

    Not directly comparable

  • MATH-500

    Inkling—
    o4-mini97.5%
    Source

    Not directly comparable

30 public results · 1 shared

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Last updated October 14, 2026