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BenchLM
Model A
Claude Haiku 4.5

Anthropic

56.1/100

Estimated · Public rank #84

90% interval 44.6–67.6

Claude Haiku 4.5 vs GPT-5.2

Updated August 13, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
GPT-5.2

OpenAI

57.8/100

Estimated · Public rank #74

90% interval 49.6–66.0

Decision reading

GPT-5.2 has the higher public score estimate, 57.83 versus 56.11, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

4 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

    GPT-5.2 has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    Claude Haiku 4.5

    Claude Haiku 4.5 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

    Claude Haiku 4.5

    Claude Haiku 4.5 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

    The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.

    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

  • 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. Claude Haiku 4.5 does not fit this workload in one request. GPT-5.2 has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

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
4
Claude Haiku 4.5 only
2
GPT-5.2 only
11
Like-for-like categories
1 / 8

1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Math

Like-for-like
Claude Haiku 4.5
4.9
GPT-5.2
35.2
Weighted basis
2 vs 2 rows
Reading
GPT-5.2 leads

Coding

Directional only
Claude Haiku 4.5
73.3
GPT-5.2
70.6
Weighted basis
1 vs 2 rows
Reading
Directional only

Agentic

Not comparable
Claude Haiku 4.5
Not measured
GPT-5.2
55.7
Weighted basis
0 vs 2 rows
Reading
Not comparable

Reasoning

Not comparable
Claude Haiku 4.5
Not measured
GPT-5.2
52.9
Weighted basis
0 vs 1 rows
Reading
Not comparable

Knowledge

Not comparable
Claude Haiku 4.5
Not measured
GPT-5.2
92.4
Weighted basis
0 vs 1 rows
Reading
Not comparable

Multilingual

Not comparable
Claude Haiku 4.5
Not measured
GPT-5.2
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Claude Haiku 4.5
Not measured
GPT-5.2
80.4
Weighted basis
0 vs 2 rows
Reading
Not comparable

Instruction following

Not comparable
Claude Haiku 4.5
Not measured
GPT-5.2
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.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

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

Claude Haiku 4.5
$0.0035
Fits in one request
GPT-5.2
$0.00875
Fits in one request

Claude Haiku 4.5 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Claude Haiku 4.5
$0.065
Fits in one request
GPT-5.2
$0.1295
Fits in one request

Claude Haiku 4.5 has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

Claude Haiku 4.5
$0.09
Does not fit in one request
GPT-5.2
$0.525
Fits in one request
Cached input priced at the published list-input rate

Claude Haiku 4.5 does not fit this workload in one request. GPT-5.2 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.

Claude Haiku 4.5

200K

Claude API pricing

GPT-5.2

400K

Cached-input rate

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

Claude Haiku 4.5

$0.1 per 1M cached input tokens

Claude API pricing

GPT-5.2

Not published

Documented inputs

Claude Haiku 4.5

Not sourced

GPT-5.2

Not sourced

Documented outputs

Claude Haiku 4.5

Not sourced

GPT-5.2

Not sourced

Provider availability

Claude Haiku 4.5

Not sourced

GPT-5.2

Not sourced

Reasoning profile

Claude Haiku 4.5

Non-Reasoning

GPT-5.2

Reasoning

Weight access

Claude Haiku 4.5

Proprietary

GPT-5.2

Proprietary

License

Claude Haiku 4.5

Proprietary

GPT-5.2

Proprietary

Release date

Claude Haiku 4.5

2025-10-15

GPT-5.2

2025-12-11

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-5.2 has the higher public score estimate, 57.83 versus 56.11, but the 90% score intervals overlap.
Workload cost
Repository review: $0.065 vs $0.1295. Cache-heavy agent loop: $0.09 vs $0.525.
Context tradeoff
GPT-5.2 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 evidence17 rows

Agentic

  • JobBench

    Shared source
    Claude Haiku 4.516.0%
    GPT-5.234.3%

    GPT-5.2 leads this result

  • BrowseComp

    Claude Haiku 4.5—
    GPT-5.265.8%
    Source

    Not directly comparable

  • OSWorld-Verified

    Claude Haiku 4.5—
    GPT-5.247.3%
    Source

    Not directly comparable

  • Gert Labs

    Claude Haiku 4.5—
    GPT-5.246.54%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Claude Haiku 4.573.3%
    Source
    GPT-5.280%
    Source

    GPT-5.2 leads this result

  • VulcanBench v3

    Claude Haiku 4.578.3%
    Source
    GPT-5.2—

    Not directly comparable

  • EEBench

    Claude Haiku 4.53.5%
    Source
    GPT-5.2—

    Not directly comparable

  • SWE-bench Pro

    Claude Haiku 4.5—
    GPT-5.255.6%
    Source

    Not directly comparable

  • Vibe Code Bench

    Claude Haiku 4.5—
    GPT-5.253.50%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    Claude Haiku 4.5—
    GPT-5.252.9%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Claude Haiku 4.5—
    GPT-5.292.4%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Shared source
    Claude Haiku 4.55.903%
    GPT-5.240.700%

    GPT-5.2 leads this result

  • FrontierMath v2 (Tier 4)

    Shared source
    Claude Haiku 4.52.083%
    GPT-5.218.800%

    GPT-5.2 leads this result

Multimodal

  • MMMU-Pro

    Claude Haiku 4.5—
    GPT-5.279.5%
    Source

    Not directly comparable

  • MathVision

    Claude Haiku 4.5—
    GPT-5.283.0%
    Source

    Not directly comparable

  • CharXiv

    Claude Haiku 4.5—
    GPT-5.282.1%
    Source

    Not directly comparable

  • V*

    Claude Haiku 4.5—
    GPT-5.275.9%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Claude Haiku 4.5 or GPT-5.2?

GPT-5.2 has the higher public score estimate, 57.83 versus 56.11, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Claude Haiku 4.5 or GPT-5.2?

The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, Claude Haiku 4.5 or GPT-5.2?

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, Claude Haiku 4.5 or GPT-5.2?

For the stated presets, chat costs $0.0035 on Claude Haiku 4.5 and $0.00875 on GPT-5.2; repository review costs $0.065 and $0.1295; the cache-heavy agent loop costs $0.09 and $0.525. Claude Haiku 4.5 does not fit this workload in one request. GPT-5.2 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Claude Haiku 4.5 or GPT-5.2?

GPT-5.2 has the larger documented context window: 400K, compared with 200K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 13, 2026

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