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BenchLM
Model A
Agents-A1

InternScience

61.3/100

Estimated · Public rank #53

90% interval 51.4–71.1

Agents-A1 vs GPT-5.6 Sol

Updated August 21, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
GPT-5.6 Sol

OpenAI

81.7/100

Supported · Public rank #4

90% interval 78.5–85.0

Decision reading

GPT-5.6 Sol has the higher public score, 81.73 versus 61.25, and the 90% score intervals do not overlap.

1 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-5.6 Sol

    GPT-5.6 Sol 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

    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

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

    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: listed-rates

  • 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

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
1
Agents-A1 only
5
GPT-5.6 Sol only
24
Like-for-like categories
0 / 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.

Agentic

Directional only
Agents-A1
75.5
GPT-5.6 Sol
92.0
Weighted basis
1 vs 2 rows
Reading
Directional only

Coding

Not comparable
Agents-A1
Not measured
GPT-5.6 Sol
64.6
Weighted basis
0 vs 1 rows
Reading
Not comparable

Reasoning

Not comparable
Agents-A1
60.2
GPT-5.6 Sol
92.5
Weighted basis
1 vs 1 rows
Reading
Not comparable

Knowledge

Not comparable
Agents-A1
47.6
GPT-5.6 Sol
94.6
Weighted basis
1 vs 1 rows
Reading
Not comparable

Math

Not comparable
Agents-A1
Not measured
GPT-5.6 Sol
87.5
Weighted basis
0 vs 2 rows
Reading
Not comparable

Multilingual

Not comparable
Agents-A1
Not measured
GPT-5.6 Sol
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Agents-A1
Not measured
GPT-5.6 Sol
83.0
Weighted basis
0 vs 1 rows
Reading
Not comparable

Instruction following

Not comparable
Agents-A1
94.8
GPT-5.6 Sol
Not measured
Weighted basis
1 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

Agents-A1
API rate not published
Fits in one request
GPT-5.6 Sol
$0.02
Fits in one request

Agents-A1 has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Agents-A1
API rate not published
Fits in one request
GPT-5.6 Sol
$0.34
Fits in one request

Agents-A1 has no comparable published API token rate.

Cache-heavy agent loop

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

Agents-A1
API rate not published
Fits in one request
Cached-input rate unavailable
GPT-5.6 Sol
$0.5
Fits in one request

Agents-A1 has no comparable published API token 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.

Agents-A1

262K

GPT-5.6 Sol

1.05M

OpenAI model catalog

Cached-input rate

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

Agents-A1

No comparable hosted API rate

GPT-5.6 Sol

$0.5 per 1M cached input tokens

OpenAI pricing

Provider availability

Agents-A1

Not sourced

GPT-5.6 Sol

Generally Available · OpenAI Responses API

OpenAI model catalog

Reasoning profile

Agents-A1

Reasoning

GPT-5.6 Sol

Reasoning

Weight access

Agents-A1

Open Weight

GPT-5.6 Sol

Proprietary

License

Agents-A1

Open Weight

GPT-5.6 Sol

Proprietary

Release date

Agents-A1

2026-06-26

GPT-5.6 Sol

2026-07-09

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-5.6 Sol has the higher public score, 81.73 versus 61.25, and the 90% score intervals do not overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
GPT-5.6 Sol has the larger documented window (1.05M).

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 evidence30 rows

Agentic

  • BrowseComp

    Agents-A175.5%
    Source
    GPT-5.6 Sol92.2%
    Source

    GPT-5.6 Sol leads this result

  • HLE w/ tools

    Agents-A147.6%
    Source
    GPT-5.6 Sol—

    Not directly comparable

  • VITA-Bench

    Agents-A138.8%
    Source
    GPT-5.6 Sol—

    Not directly comparable

  • Terminal-Bench 3.0

    Agents-A1—
    GPT-5.6 Sol34.6%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    Agents-A1—
    GPT-5.6 Sol91.9%
    Source

    Not directly comparable

  • OSWorld 2.0

    Agents-A1—
    GPT-5.6 Sol62.6%
    Source

    Not directly comparable

  • CyberGym

    Agents-A1—
    GPT-5.6 Sol84.5%
    Source

    Not directly comparable

  • ExploitGym

    Agents-A1—
    GPT-5.6 Sol33.7%
    Source

    Not directly comparable

  • Toolathlon

    Agents-A1—
    GPT-5.6 Sol58%
    Source

    Not directly comparable

Coding

  • SWE-bench Pro

    Agents-A1—
    GPT-5.6 Sol64.6%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    Agents-A1—
    GPT-5.6 Sol91.9%
    Source

    Not directly comparable

  • deepSwe

    Agents-A1—
    GPT-5.6 Sol72.7%
    Source

    Not directly comparable

  • FrontierCode 1.1 Extended

    Agents-A1—
    GPT-5.6 Sol60.6%
    Source

    Not directly comparable

  • cursorBench32

    Agents-A1—
    GPT-5.6 Sol67.2%
    Source

    Not directly comparable

  • VulcanBench v3

    Agents-A1—
    GPT-5.6 Sol87.0%
    Source

    Not directly comparable

Reasoning

  • LongBench v2

    Agents-A160.2%
    Source
    GPT-5.6 Sol—

    Not directly comparable

  • ARC-AGI-2

    Agents-A1—
    GPT-5.6 Sol92.5%
    Source

    Not directly comparable

  • ARC-AGI-3

    Agents-A1—
    GPT-5.6 Sol7.8%
    Source

    Not directly comparable

  • GeneBench-Pro

    Agents-A1—
    GPT-5.6 Sol28.7%
    Source

    Not directly comparable

Knowledge

  • HLE

    Agents-A147.6%
    Source
    GPT-5.6 Sol—

    Not directly comparable

  • GPQA

    Agents-A1—
    GPT-5.6 Sol94.6%
    Source

    Not directly comparable

  • GPQA-D

    Agents-A1—
    GPT-5.6 Sol94.6%
    Source

    Not directly comparable

  • HealthBench Professional

    Agents-A1—
    GPT-5.6 Sol60.5%
    Source

    Not directly comparable

  • HealthBench Hard

    Agents-A1—
    GPT-5.6 Sol33.1%
    Source

    Not directly comparable

Math

  • FrontierMath (legacy)

    Agents-A1—
    GPT-5.6 Sol89%
    Source

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    Agents-A1—
    GPT-5.6 Sol89.000%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Agents-A1—
    GPT-5.6 Sol83.000%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    Agents-A1—
    GPT-5.6 Sol83%
    Source

    Not directly comparable

  • MMMU-Pro w/ Python

    Agents-A1—
    GPT-5.6 Sol84.6%
    Source

    Not directly comparable

Instruction following

  • IFEval

    Agents-A194.8%
    Source
    GPT-5.6 Sol—

    Not directly comparable

Frequently asked questions

Which is better, Agents-A1 or GPT-5.6 Sol?

GPT-5.6 Sol has the higher public score, 81.73 versus 61.25, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, Agents-A1 or GPT-5.6 Sol?

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, Agents-A1 or GPT-5.6 Sol?

The current agentic tasks 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 costs less, Agents-A1 or GPT-5.6 Sol?

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, Agents-A1 or GPT-5.6 Sol?

GPT-5.6 Sol has the larger documented context window: 1.05M, compared with 262K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 21, 2026

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