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Model comparison

GPT-5.2 Pro vs Grok Code Fast 1

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

GPT-5.2 Pro

OpenAI

—

Evidence status unavailable

90% interval unavailable

Grok Code Fast 1

xAI

37.8/100

Supported · Public rank #189

90% interval 34.8–40.7

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

    GPT-5.2 Pro

    GPT-5.2 Pro has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    Grok Code Fast 1

    Grok Code Fast 1 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

    Grok Code Fast 1

    Grok Code Fast 1 has the lower estimated token cost for this stated workload. GPT-5.2 Pro has no published cached-input rate, so cached tokens use its listed input rate. Grok Code Fast 1 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

    Grok Code Fast 1

    Grok Code Fast 1 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-5.2 Pro only
0
Grok Code Fast 1 only
1
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-5.2 Pro
Not measured
Grok Code Fast 1
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Coding

Not comparable
GPT-5.2 Pro
Not measured
Grok Code Fast 1
70.8
Weighted basis
0 vs 1 rows
Reading
Not comparable

Reasoning

Not comparable
GPT-5.2 Pro
Not measured
Grok Code Fast 1
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
GPT-5.2 Pro
Not measured
Grok Code Fast 1
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
GPT-5.2 Pro
Not measured
Grok Code Fast 1
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
GPT-5.2 Pro
Not measured
Grok Code Fast 1
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
GPT-5.2 Pro
Not measured
Grok Code Fast 1
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
GPT-5.2 Pro
Not measured
Grok Code Fast 1
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-5.2 Pro
$0.1
Fits in one request
Grok Code Fast 1
$0.00095
Fits in one request

Grok Code Fast 1 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

GPT-5.2 Pro
$1.70
Fits in one request
Grok Code Fast 1
$0.0145
Fits in one request

Grok Code Fast 1 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-5.2 Pro
$7.00
Fits in one request
Cached input priced at the published list-input rate
Grok Code Fast 1
$0.059
Fits in one request
Cached input priced at the published list-input rate

Grok Code Fast 1 has the lower modeled cost

GPT-5.2 Pro has no published cached-input rate, so cached tokens use its listed input rate. Grok Code Fast 1 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.

Context window

Maximum documented context; output-token limits may be lower.

GPT-5.2 Pro

400K

Grok Code Fast 1

256K

API model ID

GPT-5.2 Pro

Not sourced

Grok Code Fast 1

Not sourced

Cached-input rate

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

GPT-5.2 Pro

Not published

Grok Code Fast 1

Not published

Documented inputs

GPT-5.2 Pro

Not sourced

Grok Code Fast 1

Not sourced

Documented outputs

GPT-5.2 Pro

Not sourced

Grok Code Fast 1

Not sourced

Provider availability

GPT-5.2 Pro

Not sourced

Grok Code Fast 1

Not sourced

Reasoning profile

GPT-5.2 Pro

Reasoning

Grok Code Fast 1

Non-Reasoning

Weight access

GPT-5.2 Pro

Proprietary

Grok Code Fast 1

Proprietary

License

GPT-5.2 Pro

Proprietary

Grok Code Fast 1

Proprietary

Release date

GPT-5.2 Pro

2025-12-11

Grok Code Fast 1

2025-08-28

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: $1.70 vs $0.0145. Cache-heavy agent loop: $7.00 vs $0.059.
Context tradeoff
GPT-5.2 Pro has the larger documented window (400K).

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 evidence1 rows

Coding

  • SWE-bench Verified

    GPT-5.2 Pro—
    Grok Code Fast 170.8%
    Source

    Not directly comparable

Frequently asked questions

Which is better, GPT-5.2 Pro or Grok Code Fast 1?

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-5.2 Pro or Grok Code Fast 1?

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-5.2 Pro or Grok Code Fast 1?

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-5.2 Pro or Grok Code Fast 1?

For the stated presets, chat costs $0.1 on GPT-5.2 Pro and $0.00095 on Grok Code Fast 1; repository review costs $1.70 and $0.0145; the cache-heavy agent loop costs $7.00 and $0.059. GPT-5.2 Pro has no published cached-input rate, so cached tokens use its listed input rate. Grok Code Fast 1 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, GPT-5.2 Pro or Grok Code Fast 1?

GPT-5.2 Pro has the larger documented context window: 400K, compared with 256K.

Related comparisons

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Last updated July 28, 2026

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