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BenchLM
Data

GPT-OSS 20B vs Inkling

Updated October 17, 2026. Rank says Inkling is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

Inkling has the higher public score estimate, 52.6 versus 33.32, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
OpenAI logo

OpenAI

33.32/100

Supported · Public rank #166

90% interval 20.0–46.6

Model B
Thinking Machines Lab logo

Thinking Machines Lab

52.6/100

Supported · Public rank #85

90% interval 42.3–62.9

Shared results
3
GPT-OSS 20B only
4
Inkling only
21
Like-for-like categories
1 / 8
Supported: GPT-OSS 20B and 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
Show secondary and unsupported calls
  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    GPT-OSS 20B is 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

    GPT-OSS 20B is scored on Estimated evidence for agentic, so the reading is 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

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. GPT-OSS 20B does not fit this workload in one request. GPT-OSS 20B has no comparable published API token rate.

    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

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.

18.2GPT-OSS 20B33.9Inkling

Directional only · BenchAlign v5.8

Inkling has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

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.

3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Knowledge

Like-for-like
GPT-OSS 20B
33.9
Supported · #140/184
Inkling
54.6
Supported · #63/184
Basis
BenchAlign v5.8 lane · 3 vs 6 public rows
Reading
Inkling leads

Agentic

Directional only
GPT-OSS 20B
14.9
Estimated · #115/128
Inkling
37.2
Supported · #74/128
Basis
BenchAlign v5.8 lane · 0 vs 5 public rows
Reading
Directional only

Coding

Directional only
GPT-OSS 20B
18.2
Estimated · #123/150
Inkling
33.9
Supported · #82/150
Basis
BenchAlign v5.8 lane · 2 vs 6 public rows
Reading
Directional only

Instruction following

Directional only
GPT-OSS 20B
79.2
#60/148
Inkling
87.6
#36/148
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Directional only

Reasoning

Not comparable
GPT-OSS 20B
49.7
Unranked · 2 rankable rows
Inkling
79.0
#14/28
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
GPT-OSS 20B
Not ranked
Inkling
67.5
#35/54
Basis
Provisional lane · 0 vs 2 weighted rows
Reading
Not comparable

Multilingual

Not comparable
GPT-OSS 20B
Not ranked
Inkling
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
GPT-OSS 20B
Not ranked
Inkling
58.3
#11/12
Basis
Provisional lane · 0 vs 3 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

GPT-OSS 20B
Self-hosted; infrastructure cost varies
Fits in one request
Inkling
$0.00421
Fits in one request

GPT-OSS 20B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GPT-OSS 20B
Self-hosted; infrastructure cost varies
Fits in one request
Inkling
$0.10754
Fits in one request

GPT-OSS 20B has no comparable published API token rate.

Cache-heavy agent loop

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

GPT-OSS 20B
Self-hosted; infrastructure cost varies
Does not fit in one request
Cached-input rate unavailable
Inkling
$0.159
Fits in one request

GPT-OSS 20B does not fit this workload in one request. GPT-OSS 20B has no comparable published API token 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.

GPT-OSS 20B

128K

Inkling

1M

API model ID

GPT-OSS 20B

Not sourced

Inkling

Not sourced

Cached-input rate

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

GPT-OSS 20B

No comparable hosted API rate

Inkling

$0.374 per 1M cached input tokens

Documented inputs

GPT-OSS 20B

Not sourced

Inkling

Not sourced

Documented outputs

GPT-OSS 20B

Not sourced

Inkling

Not sourced

Provider availability

GPT-OSS 20B

Not sourced

Inkling

Not sourced

Reasoning profile

GPT-OSS 20B

Non-Reasoning

Inkling

Hybrid

Weight access

GPT-OSS 20B

Open Weight

Inkling

Open Weight

License

GPT-OSS 20B

Open Weight

Inkling

Open Weight

Release date

GPT-OSS 20B

2025-08-05

Inkling

2026-07-15

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 estimate, 52.6 versus 33.32, but the 90% score intervals overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
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, GPT-OSS 20B or Inkling?

Inkling has the higher public score estimate, 52.6 versus 33.32, 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-OSS 20B or Inkling?

Inkling scores higher for coding on the public lane, 33.9 to 18.2. GPT-OSS 20B is 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, GPT-OSS 20B or Inkling?

Inkling scores higher for agentic tasks on the public lane, 37.2 to 14.9. GPT-OSS 20B is 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, GPT-OSS 20B or Inkling?

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-OSS 20B or Inkling?

Inkling has the larger documented context window: 1M, compared with 128K.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

GPT-OSS 20B
API / mo$0
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Inkling
API / mo$4,913
Self-host / mo$27,962
Break-even344M/day
Model the full break-even

Benchmark evidence

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

Browse raw public benchmark evidence28 rows

Agentic

  • Terminal-Bench 2.1

    GPT-OSS 20B—
    Inkling63.8%
    Source

    Not directly comparable

  • BrowseComp

    GPT-OSS 20B—
    Inkling77.1%
    Source

    Not directly comparable

  • MCP Atlas

    GPT-OSS 20B—
    Inkling74.1%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    GPT-OSS 20B—
    Inkling47.6%
    Source

    Not directly comparable

  • CWE-bench v1

    GPT-OSS 20B—
    Inkling37.0%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    GPT-OSS 20B60.7%
    Source
    Inkling77.6%
    Source

    Inkling leads this result

  • React Native Evals

    GPT-OSS 20B71%
    Source
    Inkling—

    Not directly comparable

  • SWE-bench Pro

    GPT-OSS 20B—
    Inkling54.3%
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    GPT-OSS 20B—
    Inkling63.8%
    Source

    Not directly comparable

  • FrontierSWE v2

    GPT-OSS 20B—
    Inkling4.1%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    GPT-OSS 20B—
    Inkling85.5%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    GPT-OSS 20B—
    Inkling77.6%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    GPT-OSS 20B—
    Inkling73.5%
    Source

    Not directly comparable

  • CharXiv

    GPT-OSS 20B—
    Inkling82%
    Source

    Not directly comparable

  • CharXiv w/o tools

    GPT-OSS 20B—
    Inkling78.1%
    Source

    Not directly comparable

Knowledge

  • MMLU

    GPT-OSS 20B85.3%
    Source
    Inkling—

    Not directly comparable

  • GPQA

    GPT-OSS 20B71.5%
    Source
    Inkling87.9%
    Source

    Inkling leads this result

  • HLE

    GPT-OSS 20B10.9%
    Source
    Inkling46%
    Source

    Inkling leads this result

  • GPQA-D

    GPT-OSS 20B—
    Inkling87.9%
    Source

    Not directly comparable

  • HLE w/o tools

    GPT-OSS 20B—
    Inkling30%
    Source

    Not directly comparable

  • GPQA Diamond (Vals)

    GPT-OSS 20B—
    Inkling87.1%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    GPT-OSS 20B—
    Inkling86.3%
    Source

    Not directly comparable

Instruction following

  • IFBench

    GPT-OSS 20B—
    Inkling79.8%
    Source

    Not directly comparable

Math

  • AIME 2024

    GPT-OSS 20B92.1%
    Source
    Inkling—

    Not directly comparable

  • AIME 2025

    GPT-OSS 20B91.7%
    Source
    Inkling—

    Not directly comparable

  • AIME26

    GPT-OSS 20B—
    Inkling97.1%
    Source

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    GPT-OSS 20B—
    Inkling33.333%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    GPT-OSS 20B—
    Inkling4.878%
    Source

    Not directly comparable

28 public results · 3 shared

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Last updated October 17, 2026