Skip to main content
BenchLM

GPT-5.3 Codex vs Grok 4 Fast (Reasoning)

Updated September 29, 2026. Rank says GPT-5.3 Codex is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Share or export
Share on XLinkedInSocial cardCSVJSON

Decision reading

GPT-5.3 Codex has the higher public score estimate, 61.81 versus 42.24, but the 90% score intervals overlap. Treat that as a lead, not a settled 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
OpenAI logo

OpenAI

61.81/100

Supported · Public rank #42

90% interval 54.5–69.1

Model B
xAI logo

xAI

42.24/100

Estimated · Public rank #115

90% interval 24.4–60.1

Shared results
1
GPT-5.3 Codex only
9
Grok 4 Fast (Reasoning) only
0
Like-for-like categories
0 / 8
Supported: GPT-5.3 Codex · Estimated: Grok 4 Fast (Reasoning)How 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

    Grok 4 Fast (Reasoning)

    Grok 4 Fast (Reasoning) 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

    Grok 4 Fast (Reasoning) 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

    Grok 4 Fast (Reasoning) is not ranked on the public lane for agentic, so no winner is named for agentic.

    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

    A complete comparable API-rate estimate is not available for both models.

    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

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.

55.5GPT-5.3 Codex23.3Grok 4 Fast (Reasoning)

Directional only · BenchAlign v5.7

GPT-5.3 Codex scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.

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.

A shared-evidence shape is not available.

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

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.7 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.

Coding

Directional only
GPT-5.3 Codex
55.5
Supported · #29/143
Grok 4 Fast (Reasoning)
23.3
Estimated · #113/143
Basis
BenchAlign v5.7 lane · 6 vs 1 public rows
Reading
Directional only

Knowledge

Directional only
GPT-5.3 Codex
63.8
Estimated · #31/169
Grok 4 Fast (Reasoning)
39.7
Estimated · #100/169
Basis
BenchAlign v5.7 lane · 0 vs 0 public rows
Reading
Directional only

Instruction following

Directional only
GPT-5.3 Codex
91.2
#14/124
Grok 4 Fast (Reasoning)
58.7
#74/124
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Directional only

Agentic

Not comparable
GPT-5.3 Codex
54.1
Estimated · #34/117
Grok 4 Fast (Reasoning)
Not ranked
Basis
BenchAlign v5.7 lane · 4 vs 0 public rows
Reading
Not comparable

Reasoning

Not comparable
GPT-5.3 Codex
79.6
Unranked · 2 rankable rows
Grok 4 Fast (Reasoning)
73.0
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
GPT-5.3 Codex
76.7
Unranked · 1 rankable row
Grok 4 Fast (Reasoning)
52.2
Unranked · 1 rankable row
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
GPT-5.3 Codex
Not ranked
Grok 4 Fast (Reasoning)
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
GPT-5.3 Codex
Not ranked
Grok 4 Fast (Reasoning)
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 v5.7) 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-5.3 Codex
$0.00875
Fits in one request
Grok 4 Fast (Reasoning)
API rate not published
Fits in one request

Grok 4 Fast (Reasoning) has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GPT-5.3 Codex
$0.1295
Fits in one request
Grok 4 Fast (Reasoning)
API rate not published
Fits in one request

Grok 4 Fast (Reasoning) has no comparable published API token rate.

Cache-heavy agent loop

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

GPT-5.3 Codex
$0.525
Fits in one request
Cached input priced at the published list-input rate
Grok 4 Fast (Reasoning)
API rate not published
Fits in one request
Cached-input rate unavailable

GPT-5.3 Codex has no published cached-input rate, so cached tokens use its listed input rate. Grok 4 Fast (Reasoning) 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.

Cached-input rate

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

GPT-5.3 Codex

Not published

Grok 4 Fast (Reasoning)

No comparable hosted API rate

Provider availability

GPT-5.3 Codex

Generally Available · OpenAI Responses API

OpenAI model catalog

Grok 4 Fast (Reasoning)

Not sourced

Reasoning profile

GPT-5.3 Codex

Reasoning

Grok 4 Fast (Reasoning)

Reasoning

Weight access

GPT-5.3 Codex

Proprietary

Grok 4 Fast (Reasoning)

Proprietary

License

GPT-5.3 Codex

Proprietary

Grok 4 Fast (Reasoning)

Proprietary

Release date

GPT-5.3 Codex

2026-02-05

Grok 4 Fast (Reasoning)

2025-09-19

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
GPT-5.3 Codex has the higher public score estimate, 61.81 versus 42.24, but the 90% score intervals overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Grok 4 Fast (Reasoning) has the larger documented window (2M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, GPT-5.3 Codex or Grok 4 Fast (Reasoning)?

GPT-5.3 Codex has the higher public score estimate, 61.81 versus 42.24, 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-5.3 Codex or Grok 4 Fast (Reasoning)?

GPT-5.3 Codex scores higher for coding on the public lane, 55.5 to 23.3. Grok 4 Fast (Reasoning) 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-5.3 Codex or Grok 4 Fast (Reasoning)?

Grok 4 Fast (Reasoning) is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, GPT-5.3 Codex or Grok 4 Fast (Reasoning)?

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-5.3 Codex or Grok 4 Fast (Reasoning)?

Grok 4 Fast (Reasoning) has the larger documented context window: 2M, compared with 400K.

Benchmark evidence

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

Browse raw public benchmark evidence10 rows

Agentic

  • Terminal-Bench 2.0

    GPT-5.3 Codex77.3%
    Source
    Grok 4 Fast (Reasoning)—

    Not directly comparable

  • OSWorld-Verified

    GPT-5.3 Codex64.7%
    Source
    Grok 4 Fast (Reasoning)—

    Not directly comparable

  • Gert Labs

    GPT-5.3 Codex57.47%
    Source
    Grok 4 Fast (Reasoning)—

    Not directly comparable

  • JobBench

    GPT-5.3 Codex33.7%
    Source
    Grok 4 Fast (Reasoning)—

    Not directly comparable

Coding

  • SWE-bench Verified

    GPT-5.3 Codex85%
    Source
    Grok 4 Fast (Reasoning)—

    Not directly comparable

  • SWE-bench Pro

    GPT-5.3 Codex56.8%
    Source
    Grok 4 Fast (Reasoning)—

    Not directly comparable

  • SWE-Rebench

    GPT-5.3 Codex58.2%
    Source
    Grok 4 Fast (Reasoning)—

    Not directly comparable

  • Vibe Code Bench

    Shared source
    GPT-5.3 Codex61.77%
    Grok 4 Fast (Reasoning)0.00%

    GPT-5.3 Codex leads this result

  • LiveCodeBench (Vals)

    GPT-5.3 Codex87.3%
    Source
    Grok 4 Fast (Reasoning)—

    Not directly comparable

  • SWE-bench (Vals)

    GPT-5.3 Codex78.0%
    Source
    Grok 4 Fast (Reasoning)—

    Not directly comparable

10 public results · 1 shared

Watch GPT-5.3 Codex vs Grok 4 Fast (Reasoning)

One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.

Read a sample issue

Join 2,000+ readers.

Last updated September 29, 2026