Skip to main content
Radar

Every change to the models you run, with its source and its date. Releases, price changes, retirements, API changes, and incidents.Every change to the models you run, with its source.

Follow model changes
BenchLM

Qwen3.5 Flash vs Step 5 Preview

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.

Alibaba logo
Model A
Qwen3.5 Flash

Alibaba

53.71/100

Supported · Public rank #108

90% interval 43.8–63.6

StepFun logo
Model B
Step 5 Preview

StepFun

—

Evidence status unavailable

90% interval unavailable

Updated September 21, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

Share or export

Share on XLinkedInSocial cardCSVJSON

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.

  • Chat turn cost

    1K fresh input + 500 output tokens

    Qwen3.5 Flash

    Qwen3.5 Flash 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

    Qwen3.5 Flash

    Qwen3.5 Flash has the lower estimated token cost for this stated workload. Qwen3.5 Flash 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

    Qwen3.5 Flash

    Qwen3.5 Flash 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

    Step 5 Preview is not ranked on the public lane for coding, so no winner is named for coding.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    Qwen3.5 Flash and Step 5 Preview are not ranked on the public lane for agentic, so no winner is named for agentic.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented

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.

39.2Qwen3.5 Flash—Step 5 Preview

Not comparable · BenchAlign

The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboardShortlist models for this job

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
Qwen3.5 Flash only
6
Step 5 Preview only
22
Like-for-like categories
0 / 8

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign 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

Not comparable
Qwen3.5 Flash
Not ranked
Step 5 Preview
Not ranked
Basis
BenchAlign lane · 0 vs 11 public rows
Reading
Not comparable

Coding

Not comparable
Qwen3.5 Flash
39.2
Estimated · #126/156
Step 5 Preview
Not ranked
Basis
BenchAlign lane · 2 vs 6 public rows
Reading
Not comparable

Reasoning

Not comparable
Qwen3.5 Flash
Not ranked
Step 5 Preview
78.5
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Qwen3.5 Flash
Not ranked
Step 5 Preview
61.2
#28/49
Basis
Provisional lane · 0 vs 2 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Qwen3.5 Flash
49.6
Estimated · #86/186
Step 5 Preview
Not ranked
Basis
BenchAlign lane · 2 vs 2 public rows
Reading
Not comparable

Multilingual

Not comparable
Qwen3.5 Flash
Not ranked
Step 5 Preview
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Qwen3.5 Flash
Not ranked
Step 5 Preview
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Qwen3.5 Flash
28.4
Unranked · 2 rankable rows
Step 5 Preview
Not ranked
Basis
Provisional lane · 2 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) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

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

Qwen3.5 Flash
$0.0003
Fits in one request
Step 5 Preview
$0.00235
Fits in one request

Qwen3.5 Flash has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Qwen3.5 Flash
$0.0062
Fits in one request
Step 5 Preview
$0.0581
Fits in one request

Qwen3.5 Flash has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

Qwen3.5 Flash
$0.026
Fits in one request
Cached input priced at the published list-input rate
Step 5 Preview
$0.057
Fits in one request

Qwen3.5 Flash has the lower modeled cost

Qwen3.5 Flash 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.

Cached-input rate

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

Qwen3.5 Flash

Not published

Step 5 Preview

$0.05 per 1M cached input tokens

StepFun pricing and rate limits

Reasoning profile

Qwen3.5 Flash

Reasoning

Step 5 Preview

Reasoning

Weight access

Qwen3.5 Flash

Proprietary

Step 5 Preview

Pending

License

Qwen3.5 Flash

Proprietary

Step 5 Preview

Pending

Release date

Qwen3.5 Flash

2026-03-04

Step 5 Preview

2026-09-20

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: $0.0062 vs $0.0581. Cache-heavy agent loop: $0.026 vs $0.057.
Context tradeoff
Both models list 1M.

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 evidence28 rows

Agentic

  • Terminal-Bench 2.1

    Qwen3.5 Flash—
    Step 5 Preview85.0%
    Source

    Not directly comparable

  • Terminal-Bench 4.0

    Qwen3.5 Flash—
    Step 5 Preview33.30%
    Source

    Not directly comparable

  • CyberGym

    Qwen3.5 Flash—
    Step 5 Preview84.7%
    Source

    Not directly comparable

  • AutomationBench

    Qwen3.5 Flash—
    Step 5 Preview44.0%
    Source

    Not directly comparable

  • Toolathlon-Verified

    Qwen3.5 Flash—
    Step 5 Preview74.1%
    Source

    Not directly comparable

  • MCP Atlas

    Qwen3.5 Flash—
    Step 5 Preview85.6%
    Source

    Not directly comparable

  • JobBench

    Qwen3.5 Flash—
    Step 5 Preview59.0%
    Source

    Not directly comparable

  • APEX-Agents

    Qwen3.5 Flash—
    Step 5 Preview37.8%
    Source

    Not directly comparable

  • DRACO

    Qwen3.5 Flash—
    Step 5 Preview83.3%
    Source

    Not directly comparable

  • BrowseComp

    Qwen3.5 Flash—
    Step 5 Preview88.7%
    Source

    Not directly comparable

  • Agents' Last Exam

    Qwen3.5 Flash—
    Step 5 Preview29.5%
    Source

    Not directly comparable

Coding

  • LiveCodeBench (Vals)

    Qwen3.5 Flash83.3%
    Source
    Step 5 Preview—

    Not directly comparable

  • SWE-bench (Vals)

    Qwen3.5 Flash64.4%
    Source
    Step 5 Preview—

    Not directly comparable

  • DeepSWE

    Qwen3.5 Flash—
    Step 5 Preview67.7%
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    Qwen3.5 Flash—
    Step 5 Preview85.0%
    Source

    Not directly comparable

  • SciCode

    Qwen3.5 Flash—
    Step 5 Preview58.9%
    Source

    Not directly comparable

  • ProgramBench

    Qwen3.5 Flash—
    Step 5 Preview80.5%
    Source

    Not directly comparable

  • sweMarathon

    Qwen3.5 Flash—
    Step 5 Preview72.7%
    Source

    Not directly comparable

  • MLS-Bench Lite

    Qwen3.5 Flash—
    Step 5 Preview40.5%
    Source

    Not directly comparable

Reasoning

  • CritPt

    Qwen3.5 Flash—
    Step 5 Preview20.9%
    Source

    Not directly comparable

Multimodal

  • OfficeQA Pro

    Qwen3.5 Flash—
    Step 5 Preview60.3%
    Source

    Not directly comparable

  • MMMU-Pro

    Qwen3.5 Flash—
    Step 5 Preview76%
    Source

    Not directly comparable

Knowledge

  • GPQA Diamond (Vals)

    Qwen3.5 Flash82.8%
    Source
    Step 5 Preview—

    Not directly comparable

  • MMLU-Pro (Vals)

    Qwen3.5 Flash84.1%
    Source
    Step 5 Preview—

    Not directly comparable

  • GPQA-D

    Qwen3.5 Flash—
    Step 5 Preview93.5%
    Source

    Not directly comparable

  • HLE

    Qwen3.5 Flash—
    Step 5 Preview46.5%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Qwen3.5 Flash6.207%
    Source
    Step 5 Preview—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Qwen3.5 Flash0.000%
    Source
    Step 5 Preview—

    Not directly comparable

Questions

Which is better, Qwen3.5 Flash or Step 5 Preview?

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, Qwen3.5 Flash or Step 5 Preview?

Step 5 Preview is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Qwen3.5 Flash or Step 5 Preview?

Qwen3.5 Flash and Step 5 Preview are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Qwen3.5 Flash or Step 5 Preview?

For the stated presets, chat costs $0.0003 on Qwen3.5 Flash and $0.00235 on Step 5 Preview; repository review costs $0.0062 and $0.0581; the cache-heavy agent loop costs $0.026 and $0.057. Qwen3.5 Flash has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Qwen3.5 Flash or Step 5 Preview?

Both models list the same context window, 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 21, 2026

Watch Qwen3.5 Flash vs Step 5 Preview

One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.

Read a sample issue

Join 2,000+ readers.