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Model comparison

GPT-OSS 20B vs Grok 4.20

Updated July 28, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

GPT-OSS 20B

OpenAI

41.9/100

Supported · Public rank #167

90% interval 27.2–56.6

Grok 4.20

xAI

53.9/100

Estimated · Public rank #93

90% interval 37.1–70.6

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.

0 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

    Grok 4.20

    Grok 4.20 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

    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

  • 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. Grok 4.20 has no published cached-input rate, so cached tokens use its listed input rate. GPT-OSS 20B has no comparable published API token rate.

    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
0
GPT-OSS 20B only
1
Grok 4.20 only
18
Like-for-like categories
0 / 8

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.

Agentic

Not comparable
GPT-OSS 20B
Not measured
Grok 4.20
47.1
Weighted basis
0 vs 1 rows
Reading
Not comparable

Coding

Not comparable
GPT-OSS 20B
Not measured
Grok 4.20
67.1
Weighted basis
0 vs 2 rows
Reading
Not comparable

Reasoning

Not comparable
GPT-OSS 20B
Not measured
Grok 4.20
53.3
Weighted basis
0 vs 1 rows
Reading
Not comparable

Knowledge

Not comparable
GPT-OSS 20B
Not measured
Grok 4.20
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
GPT-OSS 20B
Not measured
Grok 4.20
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
GPT-OSS 20B
Not measured
Grok 4.20
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
GPT-OSS 20B
Not measured
Grok 4.20
70.1
Weighted basis
0 vs 2 rows
Reading
Not comparable

Instruction following

Not comparable
GPT-OSS 20B
Not measured
Grok 4.20
Not measured
Weighted basis
0 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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

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
Grok 4.20
$0.005
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
Grok 4.20
$0.118
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
Grok 4.20
$0.5
Fits in one request
Cached input priced at the published list-input rate

GPT-OSS 20B does not fit this workload in one request. Grok 4.20 has no published cached-input rate, so cached tokens use its listed input rate. GPT-OSS 20B 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-OSS 20B

128K

Grok 4.20

2M

API model ID

GPT-OSS 20B

Not sourced

Grok 4.20

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

Grok 4.20

Not published

Documented inputs

GPT-OSS 20B

Not sourced

Grok 4.20

Not sourced

Documented outputs

GPT-OSS 20B

Not sourced

Grok 4.20

Not sourced

Provider availability

GPT-OSS 20B

Not sourced

Grok 4.20

Not sourced

Reasoning profile

GPT-OSS 20B

Non-Reasoning

Grok 4.20

Reasoning

Weight access

GPT-OSS 20B

Open Weight

Grok 4.20

Proprietary

License

GPT-OSS 20B

Open Weight

Grok 4.20

Proprietary

Release date

GPT-OSS 20B

2025-08-05

Grok 4.20

2026-03-10

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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Grok 4.20 has the larger documented window (2M).

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 evidence19 rows

Agentic

  • Terminal-Bench 2.0

    GPT-OSS 20B—
    Grok 4.2047.1%
    Source

    Not directly comparable

  • DeepSearchQA

    GPT-OSS 20B—
    Grok 4.2062.8%
    Source

    Not directly comparable

  • Gert Labs

    GPT-OSS 20B—
    Grok 4.2038.36%
    Source

    Not directly comparable

Coding

  • React Native Evals

    GPT-OSS 20B71%
    Source
    Grok 4.20—

    Not directly comparable

  • LiveCodeBench Pro

    GPT-OSS 20B—
    Grok 4.2074.2%
    Source

    Not directly comparable

  • SWE-bench Verified

    GPT-OSS 20B—
    Grok 4.2076.7%
    Source

    Not directly comparable

  • SWE-bench Pro

    GPT-OSS 20B—
    Grok 4.2051.8%
    Source

    Not directly comparable

  • Vibe Code Bench

    GPT-OSS 20B—
    Grok 4.204.06%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    GPT-OSS 20B—
    Grok 4.2053.3%
    Source

    Not directly comparable

  • ARC-AGI-3

    GPT-OSS 20B—
    Grok 4.200.1%
    Source

    Not directly comparable

Knowledge

  • GPQA-D

    GPT-OSS 20B—
    Grok 4.2088.5%
    Source

    Not directly comparable

  • HLE w/o tools

    GPT-OSS 20B—
    Grok 4.2031.6%
    Source

    Not directly comparable

  • HealthBench Hard

    GPT-OSS 20B—
    Grok 4.2020.3%
    Source

    Not directly comparable

  • MedXpertQA (Text)

    GPT-OSS 20B—
    Grok 4.2050.2%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    GPT-OSS 20B—
    Grok 4.2075.2%
    Source

    Not directly comparable

  • CharXiv

    GPT-OSS 20B—
    Grok 4.2060.9%
    Source

    Not directly comparable

  • ERQA

    GPT-OSS 20B—
    Grok 4.2054.1%
    Source

    Not directly comparable

  • SimpleVQA

    GPT-OSS 20B—
    Grok 4.2057.4%
    Source

    Not directly comparable

  • MedXpertQA (MM)

    GPT-OSS 20B—
    Grok 4.2065.8%
    Source

    Not directly comparable

Frequently asked questions

Which is better, GPT-OSS 20B or Grok 4.20?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, GPT-OSS 20B or Grok 4.20?

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, GPT-OSS 20B or Grok 4.20?

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-OSS 20B or Grok 4.20?

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 Grok 4.20?

Grok 4.20 has the larger documented context window: 2M, compared with 128K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated July 28, 2026

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