Qwen3.5 397B vs Qwen3.8-Omni-Flash

Decision reading

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.

6 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Qwen3.5 397B

Alibaba

55.09/100

Estimated · Public rank #95

90% interval 43.6–66.6

Model B

Qwen3.8-Omni-Flash

Alibaba

—

Evidence status unavailable

90% interval unavailable

Updated September 18, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

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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
Qwen3.8-Omni-Flash
Qwen3.8-Omni-Flash 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
Qwen3.5 397B and Qwen3.8-Omni-Flash are 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
Qwen3.8-Omni-Flash is not ranked on the public lane for agentic, so no winner is named for agentic.
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
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Qwen3.5 397B does not fit this workload in one request. Qwen3.5 397B has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.8-Omni-Flash has no comparable published API token rate.
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.

32 Qwen3.5 397B only

6 Shared

13 Qwen3.8-Omni-Flash only

Shared results: 6
Qwen3.5 397B only: 32
Qwen3.8-Omni-Flash only: 13
Like-for-like categories: 0 / 8

3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Coding

Directional only

Qwen3.5 397B: 50.6; Estimated · #59/154
Qwen3.8-Omni-Flash: 53.5; Estimated · #45/154
Basis: BenchAlign lane · 3 vs 5 public rows
Reading: Directional only

Knowledge

Directional only

Qwen3.5 397B: 50.3; Estimated · #84/184
Qwen3.8-Omni-Flash: 54.9; Estimated · #54/184
Basis: BenchAlign lane · 6 vs 3 public rows
Reading: Directional only

Instruction following

Directional only

Qwen3.5 397B: 0.0; #124/124
Qwen3.8-Omni-Flash: 87.7; #29/124
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Directional only

Agentic

Not comparable

Qwen3.5 397B: 46.8; Estimated · #77/154
Qwen3.8-Omni-Flash: Not ranked
Basis: BenchAlign lane · 13 vs 2 public rows
Reading: Not comparable

Reasoning

Not comparable

Qwen3.5 397B: 59.8; Unranked · 2 rankable rows
Qwen3.8-Omni-Flash: Not ranked
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Qwen3.5 397B: 73.5; Unranked · 5 rankable rows
Qwen3.8-Omni-Flash: Not ranked
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Qwen3.5 397B: 69.7; #5/12
Qwen3.8-Omni-Flash: Not ranked
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Qwen3.5 397B: 62.9; #27/48
Qwen3.8-Omni-Flash: 85.1; Unranked · 7 rankable rows
Basis: Provisional lane · 2 vs 1 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Qwen3.5 397B and Qwen3.8-Omni-Flash
Category	Qwen3.5 397B	Qwen3.8-Omni-Flash	Basis	Reading
Coding	50.6Estimated · #59/154	53.5Estimated · #45/154	Directional onlyBenchAlign lane · 3 vs 5 public rows	Directional only
Knowledge	50.3Estimated · #84/184	54.9Estimated · #54/184	Directional onlyBenchAlign lane · 6 vs 3 public rows	Directional only
Instruction following	0.0#124/124	87.7#29/124	Directional onlyProvisional lane · 0 vs 1 weighted rows	Directional only
Agentic	46.8Estimated · #77/154	Not ranked	Not comparableBenchAlign lane · 13 vs 2 public rows	Not comparable
Reasoning	59.8Unranked · 2 rankable rows	Not ranked	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Math	73.5Unranked · 5 rankable rows	Not ranked	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable
Multilingual	69.7#5/12	Not ranked	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Multimodal	62.9#27/48	85.1Unranked · 7 rankable rows	Not comparableProvisional lane · 2 vs 1 weighted rows	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.

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.

SWE-bench Pro
Coding
Qwen3.5 397B: 50.9%Qwen3.8-Omni-Flash: 63.3%Normalized gap 12.4Qwen3.5 397B source Qwen3.8-Omni-Flash source
CharXiv
Multimodal
Qwen3.5 397B: 80.8%Qwen3.8-Omni-Flash: 91.4%Normalized gap 10.6Qwen3.5 397B source Qwen3.8-Omni-Flash source
HLE
Knowledge
Qwen3.5 397B: 28.7%Qwen3.8-Omni-Flash: 36.5%Normalized gap 7.8Qwen3.5 397B source Qwen3.8-Omni-Flash source
GPQA
Knowledge
Qwen3.5 397B: 88.4%Qwen3.8-Omni-Flash: 91%Normalized gap 2.6Qwen3.5 397B source Qwen3.8-Omni-Flash source

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 397B: $0.0024; Fits in one request
Qwen3.8-Omni-Flash: API rate not published; Fits in one request

Qwen3.8-Omni-Flash has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Qwen3.5 397B: $0.0408; Fits in one request
Qwen3.8-Omni-Flash: API rate not published; Fits in one request

Qwen3.8-Omni-Flash has no comparable published API token rate.

Cache-heavy agent loop

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

Qwen3.5 397B: $0.168; Does not fit in one request; Cached input priced at the published list-input rate
Qwen3.8-Omni-Flash: API rate not published; Fits in one request; Cached-input rate unavailable

Qwen3.5 397B does not fit this workload in one request. Qwen3.5 397B has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.8-Omni-Flash 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.

SpecificationQwen3.5 397BQwen3.8-Omni-Flash

Context window

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

Qwen3.5 397B

128K

Qwen3.8-Omni-Flash

Alibaba Cloud Model Studio pricing

API model ID

Qwen3.5 397B

Not sourced

Qwen3.8-Omni-Flash

qwen3.8-omni-flash

Alibaba Cloud Model Studio pricing

Cached-input rate

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

Qwen3.5 397B

Not published

Qwen3.8-Omni-Flash

No comparable hosted API rate

Alibaba Cloud Model Studio pricing

Documented inputs

Qwen3.5 397B

Not sourced

Qwen3.8-Omni-Flash

Not sourced

Documented outputs

Qwen3.5 397B

Not sourced

Qwen3.8-Omni-Flash

Not sourced

Provider availability

Qwen3.5 397B

Not sourced

Qwen3.8-Omni-Flash

Not sourced

Reasoning profile

Qwen3.5 397B

Non-Reasoning

Qwen3.8-Omni-Flash

Reasoning

Weight access

Qwen3.5 397B

Open Weight

Qwen3.8-Omni-Flash

Proprietary

License

Qwen3.5 397B

Open Weight

Qwen3.8-Omni-Flash

Proprietary

Release date

Qwen3.5 397B

2026-02-16

Qwen3.8-Omni-Flash

2026-09-18

If you already use one of these models

Deployment change: Both entries list Alibaba as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
Quality signal: At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Qwen3.8-Omni-Flash has the larger documented window (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 evidence51 rows

Agentic

Terminal-Bench 2.0
Qwen3.5 397B52.5%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
BrowseComp
Qwen3.5 397B62%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
Claw-Eval
Qwen3.5 397B56.8%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
QwenClawBench
Qwen3.5 397B51.8%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
τ³-bench results
Qwen3.5 397B68.4%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
VITA-Bench
Qwen3.5 397B43.7%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
DeepPlanning
Qwen3.5 397B37.6%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
Toolathlon
Qwen3.5 397B36.3%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
MCP Atlas
Qwen3.5 397B46.1%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
MCP-Tasks
Qwen3.5 397B74.2%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
WideResearch
Qwen3.5 397B74.0%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
Gert Labs
Qwen3.5 397B46.76%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
ResearchClawBench
Qwen3.5 397B14.2%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
CoWorkBench
Qwen3.5 397B—
Qwen3.8-Omni-Flash75.3%
Source
Not directly comparable
AndroidWorld
Qwen3.5 397B—
Qwen3.8-Omni-Flash87.1%
Source
Not directly comparable

Coding

SWE-bench Verified
Qwen3.5 397B76.2%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
LiveCodeBench v6
Qwen3.5 397B83.6%
Source
Qwen3.8-Omni-Flash92.6%
Source
Qwen3.8-Omni-Flash leads this result
SWE-bench Pro
Qwen3.5 397B50.9%
Source
Qwen3.8-Omni-Flash63.3%
Source
Qwen3.8-Omni-Flash leads this result
SWE Multilingual
Qwen3.5 397B—
Qwen3.8-Omni-Flash80.5%
Source
Not directly comparable
NL2Repo
Qwen3.5 397B—
Qwen3.8-Omni-Flash48.9%
Source
Not directly comparable
DeepSWE
Qwen3.5 397B—
Qwen3.8-Omni-Flash57.8%
Source
Not directly comparable

Reasoning

LongBench v2
Qwen3.5 397B63.2%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
AI-Needle
Qwen3.5 397B68.7%
Source
Qwen3.8-Omni-Flash—
Not directly comparable

Knowledge

GPQA
Qwen3.5 397B88.4%
Source
Qwen3.8-Omni-Flash91%
Source
Qwen3.8-Omni-Flash leads this result
SuperGPQA
Qwen3.5 397B70.4%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
MMLU-Pro
Qwen3.5 397B87.8%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
MMLU-Redux
Qwen3.5 397B94.9%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
C-Eval
Qwen3.5 397B93%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
HLE
Qwen3.5 397B28.7%
Source
Qwen3.8-Omni-Flash36.5%
Source
Qwen3.8-Omni-Flash leads this result
GPQA-D
Qwen3.5 397B—
Qwen3.8-Omni-Flash91.0%
Source
Not directly comparable

Math

AIME26
Qwen3.5 397B93.3%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
HMMT Feb 2025
Qwen3.5 397B94.8%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
HMMT Nov 2025
Qwen3.5 397B92.7%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
HMMT Feb 2026
Qwen3.5 397B87.9%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
MMAnswerBench
Qwen3.5 397B80.9%
Source
Qwen3.8-Omni-Flash—
Not directly comparable

Multilingual

MMLU-ProX
Qwen3.5 397B84.7%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
NOVA-63
Qwen3.5 397B59.1%
Source
Qwen3.8-Omni-Flash—
Not directly comparable

Multimodal

MMMU-Pro
Qwen3.5 397B79%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
MathVision
Qwen3.5 397B88.6%
Source
Qwen3.8-Omni-Flash91.8%
Source
Qwen3.8-Omni-Flash leads this result
CharXiv
Qwen3.5 397B80.8%
Source
Qwen3.8-Omni-Flash91.4%
Source
Qwen3.8-Omni-Flash leads this result
VideoMMMU
Qwen3.5 397B84.7%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
ScreenSpot Pro
Qwen3.5 397B65.6%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
V*
Qwen3.5 397B95.8%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
Vision2Web
Qwen3.5 397B—
Qwen3.8-Omni-Flash62.9%
Source
Not directly comparable
ERQA
Qwen3.5 397B—
Qwen3.8-Omni-Flash71.0%
Source
Not directly comparable
LVBench
Qwen3.5 397B—
Qwen3.8-Omni-Flash76.9%
Source
Not directly comparable
RealWorldQA
Qwen3.5 397B—
Qwen3.8-Omni-Flash87.7%
Source
Not directly comparable
MathVision w/ Python
Qwen3.5 397B—
Qwen3.8-Omni-Flash96.2%
Source
Not directly comparable
CharXiv w/o tools
Qwen3.5 397B—
Qwen3.8-Omni-Flash83.5%
Source
Not directly comparable

Instruction following

IFEval
Qwen3.5 397B92.6%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
IFBench
Qwen3.5 397B—
Qwen3.8-Omni-Flash81.5%
Source
Not directly comparable

Questions

Which is better, Qwen3.5 397B or Qwen3.8-Omni-Flash?

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Qwen3.5 397B or Qwen3.8-Omni-Flash?

Qwen3.8-Omni-Flash scores higher for coding on the public lane, 53.5 to 50.6. Qwen3.5 397B and Qwen3.8-Omni-Flash are 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, Qwen3.5 397B or Qwen3.8-Omni-Flash?

Qwen3.8-Omni-Flash is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Qwen3.5 397B or Qwen3.8-Omni-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, Qwen3.5 397B or Qwen3.8-Omni-Flash?

Qwen3.8-Omni-Flash has the larger documented context window: 1M, compared with 128K.

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Last updated September 18, 2026

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Qwen3.5 397B vs Qwen3.8-Omni-Flash

Which one for your work

Long documents

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

What is actually comparable

Category results, on a stated basis

Coding

Knowledge

Instruction following

Agentic

Reasoning

Math

Multilingual

Multimodal

Shape of the matched evidence

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Math

Multilingual

Multimodal

Instruction following

Questions

Which is better, Qwen3.5 397B or Qwen3.8-Omni-Flash?

Which is better for coding, Qwen3.5 397B or Qwen3.8-Omni-Flash?

Which is better for agentic tasks, Qwen3.5 397B or Qwen3.8-Omni-Flash?

Which costs less, Qwen3.5 397B or Qwen3.8-Omni-Flash?

Which has the larger context window, Qwen3.5 397B or Qwen3.8-Omni-Flash?

Related comparisons

Watch Qwen3.5 397B vs Qwen3.8-Omni-Flash