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Model comparison

GPT-5.4 nano vs Grok 4.3

Updated July 30, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

GPT-5.4 nano

OpenAI

66.0/100

Supported · Public rank #28

90% interval 55.5–76.4

Grok 4.3

xAI

64.2/100

Supported · Public rank #36

90% interval 54.5–74.0

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 4.3

    Grok 4.3 has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    GPT-5.4 nano

    GPT-5.4 nano 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-5.4 nano

    GPT-5.4 nano 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

    GPT-5.4 nano

    GPT-5.4 nano 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

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-5.4 nano only
13
Grok 4.3 only
2
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-5.4 nano
42.9
Grok 4.3
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Coding

Not comparable
GPT-5.4 nano
Not measured
Grok 4.3
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
GPT-5.4 nano
Not measured
Grok 4.3
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
GPT-5.4 nano
43.8
Grok 4.3
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Math

Not comparable
GPT-5.4 nano
21.0
Grok 4.3
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
GPT-5.4 nano
Not measured
Grok 4.3
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
GPT-5.4 nano
66.1
Grok 4.3
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
GPT-5.4 nano
Not measured
Grok 4.3
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-5.4 nano
$0.00082
Fits in one request
Grok 4.3
$0.0025
Fits in one request

GPT-5.4 nano has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

GPT-5.4 nano
$0.01375
Fits in one request
Grok 4.3
$0.07
Fits in one request

GPT-5.4 nano 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-5.4 nano
$0.0205
Fits in one request
Grok 4.3
$0.09
Fits in one request

GPT-5.4 nano has the lower modeled cost

Costs use the listed standard API rates.

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-5.4 nano

$0.02 per 1M cached input tokens

OpenAI pricing

Grok 4.3

$0.2 per 1M cached input tokens

Provider availability

GPT-5.4 nano

Generally Available · OpenAI Responses API

OpenAI model catalog

Grok 4.3

Not sourced

Reasoning profile

GPT-5.4 nano

Reasoning

Grok 4.3

Reasoning

Weight access

GPT-5.4 nano

Proprietary

Grok 4.3

Proprietary

License

GPT-5.4 nano

Proprietary

Grok 4.3

Proprietary

Release date

GPT-5.4 nano

2026-03-17

Grok 4.3

2026-04-30

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
Repository review: $0.01375 vs $0.07. Cache-heavy agent loop: $0.0205 vs $0.09.
Context tradeoff
Grok 4.3 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

    GPT-5.4 nano46.3%
    Source
    Grok 4.3—

    Not directly comparable

  • OSWorld-Verified

    GPT-5.4 nano39%
    Source
    Grok 4.3—

    Not directly comparable

  • MCP Atlas

    GPT-5.4 nano56.1%
    Source
    Grok 4.3—

    Not directly comparable

  • Toolathlon

    GPT-5.4 nano35.5%
    Source
    Grok 4.3—

    Not directly comparable

  • τ²-bench results

    GPT-5.4 nano92.5%
    Source
    Grok 4.3—

    Not directly comparable

  • Gert Labs

    GPT-5.4 nano—
    Grok 4.343.86%
    Source

    Not directly comparable

  • ResearchClawBench

    GPT-5.4 nano—
    Grok 4.312.4%
    Source

    Not directly comparable

Coding

  • Vibe Code Bench

    GPT-5.4 nano26.10%
    Source
    Grok 4.3—

    Not directly comparable

Knowledge

  • GPQA

    GPT-5.4 nano82.8%
    Source
    Grok 4.3—

    Not directly comparable

  • HLE

    GPT-5.4 nano37.7%
    Source
    Grok 4.3—

    Not directly comparable

  • HLE w/o tools

    GPT-5.4 nano24.3%
    Source
    Grok 4.3—

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    GPT-5.4 nano25.860%
    Source
    Grok 4.3—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    GPT-5.4 nano6.250%
    Source
    Grok 4.3—

    Not directly comparable

Multimodal

  • MMMU-Pro

    GPT-5.4 nano66.1%
    Source
    Grok 4.3—

    Not directly comparable

  • MMMU-Pro w/ Python

    GPT-5.4 nano69.5%
    Source
    Grok 4.3—

    Not directly comparable

Frequently asked questions

Which is better, GPT-5.4 nano or Grok 4.3?

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-5.4 nano or Grok 4.3?

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-5.4 nano or Grok 4.3?

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-5.4 nano or Grok 4.3?

For the stated presets, chat costs $0.00082 on GPT-5.4 nano and $0.0025 on Grok 4.3; repository review costs $0.01375 and $0.07; the cache-heavy agent loop costs $0.0205 and $0.09. Costs use the listed standard API rates.

Which has the larger context window, GPT-5.4 nano or Grok 4.3?

Grok 4.3 has the larger documented context window: 1M, compared with 400K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated July 30, 2026

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