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BenchLM
OpenAI logo
Model A
GPT-5.1-Codex

OpenAI

47.93/100

Estimated · Public rank #137

90% interval 36.4–59.4

GPT-5.1-Codex vs Grok 4.20

Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

xAI logo
Model B
Grok 4.20

xAI

67.13/100

Supported · Public rank #31

90% interval 58.5–75.8

Decision reading

Grok 4.20 has the higher public score estimate, 67.13 versus 47.93, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

2 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.

  • Long documents

    Prompts that approach the documented context limit

    Grok 4.20

    Grok 4.20 has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    Grok 4.20

    Grok 4.20 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

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

    GPT-5.1-Codex

    GPT-5.1-Codex has the lower estimated token cost for this stated workload. Grok 4.20 has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

Show secondary and unsupported calls

  • Repository review cost

    50K fresh input + 3K output tokens

    GPT-5.1-Codex

    GPT-5.1-Codex 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

    GPT-5.1-Codex 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-5.1-Codex is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    Confidence: limited

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
2
GPT-5.1-Codex only
1
Grok 4.20 only
21
Like-for-like categories
0 / 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.

Agentic

Directional only
GPT-5.1-Codex
48.6
Estimated · #63/152
Grok 4.20
26.7
Supported · #145/152
Basis
BenchAlign lane · 2 vs 4 public rows
Reading
Directional only

Coding

Directional only
GPT-5.1-Codex
44.8
Estimated · #91/151
Grok 4.20
28.2
Supported · #141/151
Basis
BenchAlign lane · 1 vs 6 public rows
Reading
Directional only

Knowledge

Directional only
GPT-5.1-Codex
51.5
Estimated · #70/183
Grok 4.20
49.4
Supported · #85/183
Basis
BenchAlign lane · 0 vs 6 public rows
Reading
Directional only

Reasoning

Not comparable
GPT-5.1-Codex
68.6
Unranked · 2 rankable rows
Grok 4.20
34.2
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 2 weighted rows
Reading
Not comparable

Math

Not comparable
GPT-5.1-Codex
Not ranked
Grok 4.20
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
GPT-5.1-Codex
Not ranked
Grok 4.20
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
GPT-5.1-Codex
66.8
Unranked · 1 rankable row
Grok 4.20
34.6
#43/48
Basis
Provisional lane · 0 vs 2 weighted rows
Reading
Not comparable

Instruction following

Not comparable
GPT-5.1-Codex
85.6
#42/123
Grok 4.20
Not ranked
Basis
Provisional lane · 0 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) 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.

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-5.1-Codex
$0.00625
Fits in one request
Grok 4.20
$0.005
Fits in one request

Grok 4.20 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

GPT-5.1-Codex
$0.0925
Fits in one request
Grok 4.20
$0.118
Fits in one request

GPT-5.1-Codex has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

GPT-5.1-Codex
$0.15
Fits in one request
Grok 4.20
$0.5
Fits in one request
Cached input priced at the published list-input rate

GPT-5.1-Codex has the lower modeled cost

Grok 4.20 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.

Cached-input rate

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

GPT-5.1-Codex

$0.125 per 1M cached input tokens

OpenAI GPT-5.1-Codex model documentation

Grok 4.20

Not published

Documented inputs

GPT-5.1-Codex

Not sourced

Grok 4.20

Not sourced

Documented outputs

GPT-5.1-Codex

Not sourced

Grok 4.20

Not sourced

Provider availability

GPT-5.1-Codex

Not sourced

Grok 4.20

Not sourced

Reasoning profile

GPT-5.1-Codex

Reasoning

Grok 4.20

Reasoning

Weight access

GPT-5.1-Codex

Proprietary

Grok 4.20

Proprietary

License

GPT-5.1-Codex

Proprietary

Grok 4.20

Proprietary

Release date

GPT-5.1-Codex

2025-10-15

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
Grok 4.20 has the higher public score estimate, 67.13 versus 47.93, but the 90% score intervals overlap.
Workload cost
Repository review: $0.0925 vs $0.118. Cache-heavy agent loop: $0.15 vs $0.5.
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 evidence24 rows

Agentic

  • Gert Labs

    Shared source
    GPT-5.1-Codex49.68%
    Grok 4.2038.36%

    GPT-5.1-Codex leads this result

  • JobBench

    GPT-5.1-Codex26.2%
    Source
    Grok 4.20—

    Not directly comparable

  • Terminal-Bench 2.0

    GPT-5.1-Codex—
    Grok 4.2047.1%
    Source

    Not directly comparable

  • DeepSearchQA

    GPT-5.1-Codex—
    Grok 4.2062.8%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    GPT-5.1-Codex—
    Grok 4.2044.2%
    Source

    Not directly comparable

Coding

  • Vibe Code Bench

    Shared source
    GPT-5.1-Codex13.12%
    Grok 4.204.06%

    GPT-5.1-Codex leads this result

  • LiveCodeBench Pro

    GPT-5.1-Codex—
    Grok 4.2074.2%
    Source

    Not directly comparable

  • SWE-bench Verified

    GPT-5.1-Codex—
    Grok 4.2076.7%
    Source

    Not directly comparable

  • SWE-bench Pro

    GPT-5.1-Codex—
    Grok 4.2051.8%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    GPT-5.1-Codex—
    Grok 4.2084.3%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    GPT-5.1-Codex—
    Grok 4.2072.2%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    GPT-5.1-Codex—
    Grok 4.2053.3%
    Source

    Not directly comparable

  • ARC-AGI-3

    GPT-5.1-Codex—
    Grok 4.200.1%
    Source

    Not directly comparable

Knowledge

  • GPQA-D

    GPT-5.1-Codex—
    Grok 4.2088.5%
    Source

    Not directly comparable

  • HLE w/o tools

    GPT-5.1-Codex—
    Grok 4.2031.6%
    Source

    Not directly comparable

  • HealthBench Hard

    GPT-5.1-Codex—
    Grok 4.2020.3%
    Source

    Not directly comparable

  • MedXpertQA (Text)

    GPT-5.1-Codex—
    Grok 4.2050.2%
    Source

    Not directly comparable

  • GPQA Diamond (Vals)

    GPT-5.1-Codex—
    Grok 4.2088.6%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    GPT-5.1-Codex—
    Grok 4.2086.3%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    GPT-5.1-Codex—
    Grok 4.2075.2%
    Source

    Not directly comparable

  • CharXiv

    GPT-5.1-Codex—
    Grok 4.2060.9%
    Source

    Not directly comparable

  • ERQA

    GPT-5.1-Codex—
    Grok 4.2054.1%
    Source

    Not directly comparable

  • SimpleVQA

    GPT-5.1-Codex—
    Grok 4.2057.4%
    Source

    Not directly comparable

  • MedXpertQA (MM)

    GPT-5.1-Codex—
    Grok 4.2065.8%
    Source

    Not directly comparable

Frequently asked questions

Which is better, GPT-5.1-Codex or Grok 4.20?

Grok 4.20 has the higher public score estimate, 67.13 versus 47.93, 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-5.1-Codex or Grok 4.20?

GPT-5.1-Codex scores higher for coding on the public lane, 44.8 to 28.2. GPT-5.1-Codex 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-5.1-Codex or Grok 4.20?

GPT-5.1-Codex scores higher for agentic tasks on the public lane, 48.6 to 26.7. GPT-5.1-Codex 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-5.1-Codex or Grok 4.20?

For the stated presets, chat costs $0.00625 on GPT-5.1-Codex and $0.005 on Grok 4.20; repository review costs $0.0925 and $0.118; the cache-heavy agent loop costs $0.15 and $0.5. Grok 4.20 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, GPT-5.1-Codex or Grok 4.20?

Grok 4.20 has the larger documented context window: 2M, compared with 400K.

Related comparisons

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Last updated September 10, 2026

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