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Data

Hy3 vs Step 3.7 Flash

Updated October 5, 2026. Rank says Hy3 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

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 resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Tencent logo

Tencent

52.62/100

Estimated · Public rank #74

Conditional range 42.9–62.3

Model B
StepFun logo

StepFun

44.15/100

Estimated · Public rank #110

Conditional range 29.8–58.5

Shared results
0
Hy3 only
0
Step 3.7 Flash only
11
Like-for-like categories
0 / 8
Estimated: Hy3 and Step 3.7 Flash. Conditional ranges do not establish rank confidence.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.

No workload recommendation clears the current evidence threshold.

Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.

Show secondary and unsupported calls
  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    Step 3.7 Flash 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

    Step 3.7 Flash is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    Confidence: limited
  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented
  • 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.

49.3Hy332.1Step 3.7 Flash

Directional only · BenchAlign v5.8

Hy3 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

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 v5.8 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.

Agentic

Directional only
Hy3
34.6
Supported · #71/119
Step 3.7 Flash
36.0
Estimated · #67/119
Basis
BenchAlign v5.8 lane · 0 vs 7 public rows
Reading
Directional only

Coding

Directional only
Hy3
49.3
Supported · #42/144
Step 3.7 Flash
32.1
Estimated · #88/144
Basis
BenchAlign v5.8 lane · 0 vs 2 public rows
Reading
Directional only

Knowledge

Directional only
Hy3
49.6
Estimated · #66/171
Step 3.7 Flash
44.1
Estimated · #85/171
Basis
BenchAlign v5.8 lane · 0 vs 0 public rows
Reading
Directional only

Reasoning

Not comparable
Hy3
76.6
Unranked · 2 rankable rows
Step 3.7 Flash
73.0
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Hy3
Not ranked
Step 3.7 Flash
72.0
Unranked · 3 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Hy3
Not ranked
Step 3.7 Flash
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Hy3
Not ranked
Step 3.7 Flash
80.6
#53/125
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Hy3
Not ranked
Step 3.7 Flash
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 v5.8) 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

Hy3
Self-hosted; infrastructure cost varies
Fits in one request
Step 3.7 Flash
$0.00077
Fits in one request

Hy3 has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Hy3
Self-hosted; infrastructure cost varies
Fits in one request
Step 3.7 Flash
$0.01345
Fits in one request

Hy3 has no comparable published API token rate.

Cache-heavy agent loop

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

Hy3
Self-hosted; infrastructure cost varies
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. Hy3 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.

Context window

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

Hy3

256K

Step 3.7 Flash

256K

API model ID

Hy3

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.

Hy3

No comparable hosted API rate

Step 3.7 Flash

Not published

Documented inputs

Hy3

Not sourced

Step 3.7 Flash

Not sourced

Documented outputs

Hy3

Not sourced

Step 3.7 Flash

Not sourced

Provider availability

Hy3

Not sourced

Step 3.7 Flash

Not sourced

Reasoning profile

Hy3

Reasoning

Step 3.7 Flash

Reasoning

Weight access

Hy3

Open Weight

Step 3.7 Flash

Open Weight

License

Hy3

Open Weight

Step 3.7 Flash

Open Weight

Release date

Hy3

2026-07-06

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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Both models list 256K.
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Hy3 or Step 3.7 Flash?

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, Hy3 or Step 3.7 Flash?

Hy3 scores higher for coding on the public lane, 49.3 to 32.1. Step 3.7 Flash 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, Hy3 or Step 3.7 Flash?

Step 3.7 Flash scores higher for agentic tasks on the public lane, 36 to 34.6. Step 3.7 Flash is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, Hy3 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, Hy3 or Step 3.7 Flash?

Both models list the same context window, 256K.

Benchmark evidence

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

Browse raw public benchmark evidence11 rows

Agentic

  • Terminal-Bench 2.1

    Hy3—
    Step 3.7 Flash59.5%
    Source

    Not directly comparable

  • BrowseComp

    Hy3—
    Step 3.7 Flash75.8%
    Source

    Not directly comparable

  • DeepSearchQA

    Hy3—
    Step 3.7 Flash92.8%
    Source

    Not directly comparable

  • Toolathlon

    Hy3—
    Step 3.7 Flash49.5%
    Source

    Not directly comparable

  • Claw-Eval

    Hy3—
    Step 3.7 Flash67.1%
    Source

    Not directly comparable

  • HLE w/ tools

    Hy3—
    Step 3.7 Flash47.2%
    Source

    Not directly comparable

  • Gert Labs

    Hy3—
    Step 3.7 Flash51.57%
    Source

    Not directly comparable

Coding

  • SWE-bench Pro

    Hy3—
    Step 3.7 Flash56.3%
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    Hy3—
    Step 3.7 Flash59.5%
    Source

    Not directly comparable

Multimodal

  • SimpleVQA

    Hy3—
    Step 3.7 Flash79.2%
    Source

    Not directly comparable

  • V*

    Hy3—
    Step 3.7 Flash95.3%
    Source

    Not directly comparable

11 public results · 0 shared

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Last updated October 5, 2026