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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 Step 3.7 Flash

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

Model B
Step 3.7 Flash

StepFun

50.8/100

Estimated · Public rank #122

90% interval 39.2–62.3

Decision reading

Agents-A1 has the higher public score estimate, 61.25 versus 50.76, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

2 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

    Agents-A1

    Agents-A1 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: 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

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
2
Agents-A1 only
4
Step 3.7 Flash only
9
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
Step 3.7 Flash
66.4
Weighted basis
1 vs 2 rows
Reading
Directional only

Coding

Not comparable
Agents-A1
Not measured
Step 3.7 Flash
56.3
Weighted basis
0 vs 1 rows
Reading
Not comparable

Reasoning

Not comparable
Agents-A1
60.2
Step 3.7 Flash
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Agents-A1
47.6
Step 3.7 Flash
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Math

Not comparable
Agents-A1
Not measured
Step 3.7 Flash
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Agents-A1
Not measured
Step 3.7 Flash
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Agents-A1
Not measured
Step 3.7 Flash
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Agents-A1
94.8
Step 3.7 Flash
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
Step 3.7 Flash
$0.00077
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
Step 3.7 Flash
$0.01345
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
Step 3.7 Flash
$0.0555
Fits in one request
Cached input priced at the published list-input rate

Step 3.7 Flash has no published cached-input rate, so cached tokens use its listed input rate. 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

Step 3.7 Flash

256K

API model ID

Agents-A1

Not sourced

Step 3.7 Flash

Not sourced

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

Step 3.7 Flash

Not published

Documented inputs

Agents-A1

Not sourced

Step 3.7 Flash

Not sourced

Documented outputs

Agents-A1

Not sourced

Step 3.7 Flash

Not sourced

Provider availability

Agents-A1

Not sourced

Step 3.7 Flash

Not sourced

Reasoning profile

Agents-A1

Reasoning

Step 3.7 Flash

Reasoning

Weight access

Agents-A1

Open Weight

Step 3.7 Flash

Open Weight

License

Agents-A1

Open Weight

Step 3.7 Flash

Open Weight

Release date

Agents-A1

2026-06-26

Step 3.7 Flash

2026-05-29

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
Agents-A1 has the higher public score estimate, 61.25 versus 50.76, but the 90% score intervals overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Agents-A1 has the larger documented window (262K).

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

  • BrowseComp

    Agents-A175.5%
    Source
    Step 3.7 Flash75.8%
    Source

    Step 3.7 Flash leads this result

  • HLE w/ tools

    Agents-A147.6%
    Source
    Step 3.7 Flash47.2%
    Source

    Agents-A1 leads this result

  • VITA-Bench

    Agents-A138.8%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • Terminal-Bench 2.0

    Agents-A1—
    Step 3.7 Flash59.5%
    Source

    Not directly comparable

  • DeepSearchQA

    Agents-A1—
    Step 3.7 Flash92.8%
    Source

    Not directly comparable

  • Toolathlon

    Agents-A1—
    Step 3.7 Flash49.5%
    Source

    Not directly comparable

  • Claw-Eval

    Agents-A1—
    Step 3.7 Flash67.1%
    Source

    Not directly comparable

  • Gert Labs

    Agents-A1—
    Step 3.7 Flash51.57%
    Source

    Not directly comparable

Coding

  • SWE-bench Pro

    Agents-A1—
    Step 3.7 Flash56.3%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    Agents-A1—
    Step 3.7 Flash59.5%
    Source

    Not directly comparable

Reasoning

  • LongBench v2

    Agents-A160.2%
    Source
    Step 3.7 Flash—

    Not directly comparable

Knowledge

  • HLE

    Agents-A147.6%
    Source
    Step 3.7 Flash—

    Not directly comparable

Multimodal

  • SimpleVQA

    Agents-A1—
    Step 3.7 Flash79.2%
    Source

    Not directly comparable

  • V*

    Agents-A1—
    Step 3.7 Flash95.3%
    Source

    Not directly comparable

Instruction following

  • IFEval

    Agents-A194.8%
    Source
    Step 3.7 Flash—

    Not directly comparable

Frequently asked questions

Which is better, Agents-A1 or Step 3.7 Flash?

Agents-A1 has the higher public score estimate, 61.25 versus 50.76, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Agents-A1 or Step 3.7 Flash?

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 Step 3.7 Flash?

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 Step 3.7 Flash?

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 Step 3.7 Flash?

Agents-A1 has the larger documented context window: 262K, compared with 256K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 21, 2026

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