Qwen3.7 Max 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.

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

Model A

Qwen3.7 Max

Alibaba

66.87/100

Supported · Public rank #31

90% interval 58.5–75.3

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.

Share or export

Share on X LinkedInSocial card CSV JSON

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
Qwen3.8-Omni-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
Qwen3.8-Omni-Flash is 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
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.

34 Qwen3.7 Max only

7 Shared

12 Qwen3.8-Omni-Flash only

Shared results: 7
Qwen3.7 Max only: 34
Qwen3.8-Omni-Flash only: 12
Like-for-like categories: 1 / 8

2 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.

Instruction following

Like-for-like

Qwen3.7 Max: 89.2; #17/124
Qwen3.8-Omni-Flash: 87.7; #29/124
Basis: Provisional lane · 1 vs 1 weighted rows
Reading: Qwen3.7 Max leads

Coding

Directional only

Qwen3.7 Max: 49.2; Supported · #68/154
Qwen3.8-Omni-Flash: 53.5; Estimated · #45/154
Basis: BenchAlign lane · 10 vs 5 public rows
Reading: Directional only

Knowledge

Directional only

Qwen3.7 Max: 61.6; Supported · #28/184
Qwen3.8-Omni-Flash: 54.9; Estimated · #54/184
Basis: BenchAlign lane · 9 vs 3 public rows
Reading: Directional only

Agentic

Not comparable

Qwen3.7 Max: 40.9; Supported · #117/154
Qwen3.8-Omni-Flash: Not ranked
Basis: BenchAlign lane · 10 vs 2 public rows
Reading: Not comparable

Reasoning

Not comparable

Qwen3.7 Max: 75.1; Unranked · 3 rankable rows
Qwen3.8-Omni-Flash: Not ranked
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Qwen3.7 Max: 81.9; Unranked · 3 rankable rows
Qwen3.8-Omni-Flash: Not ranked
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Qwen3.7 Max: 100.0; #1/12
Qwen3.8-Omni-Flash: Not ranked
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Qwen3.7 Max: Not ranked
Qwen3.8-Omni-Flash: 85.1; Unranked · 7 rankable rows
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Qwen3.7 Max and Qwen3.8-Omni-Flash
Category	Qwen3.7 Max	Qwen3.8-Omni-Flash	Basis	Reading
Instruction following	89.2#17/124	87.7#29/124	Like-for-likeProvisional lane · 1 vs 1 weighted rows	Qwen3.7 Max leads
Coding	49.2Supported · #68/154	53.5Estimated · #45/154	Directional onlyBenchAlign lane · 10 vs 5 public rows	Directional only
Knowledge	61.6Supported · #28/184	54.9Estimated · #54/184	Directional onlyBenchAlign lane · 9 vs 3 public rows	Directional only
Agentic	40.9Supported · #117/154	Not ranked	Not comparableBenchAlign lane · 10 vs 2 public rows	Not comparable
Reasoning	75.1Unranked · 3 rankable rows	Not ranked	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Math	81.9Unranked · 3 rankable rows	Not ranked	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Multilingual	100.0#1/12	Not ranked	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Multimodal	Not ranked	85.1Unranked · 7 rankable rows	Not comparableProvisional lane · 0 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.

HLE
Knowledge
Qwen3.7 Max: 41.4%Qwen3.8-Omni-Flash: 36.5%Normalized gap 4.9Qwen3.7 Max source Qwen3.8-Omni-Flash source
SWE-bench Pro
Coding
Qwen3.7 Max: 60.6%Qwen3.8-Omni-Flash: 63.3%Normalized gap 2.7Qwen3.7 Max source Qwen3.8-Omni-Flash source
IFBench
Instruction following
Qwen3.7 Max: 79.1%Qwen3.8-Omni-Flash: 81.5%Normalized gap 2.4Qwen3.7 Max source Qwen3.8-Omni-Flash source
SWE Multilingual
Coding
Qwen3.7 Max: 78.3%Qwen3.8-Omni-Flash: 80.5%Normalized gap 2.2Qwen3.7 Max source Qwen3.8-Omni-Flash source
GPQA
Knowledge
Qwen3.7 Max: 92.4%Qwen3.8-Omni-Flash: 91%Normalized gap 1.4Qwen3.7 Max 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

Qwen3.7 Max: API rate not published; Fits in one request
Qwen3.8-Omni-Flash: API rate not published; Fits in one request

Qwen3.7 Max has no comparable published API token rate. Qwen3.8-Omni-Flash has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Qwen3.7 Max: API rate not published; Fits in one request
Qwen3.8-Omni-Flash: API rate not published; Fits in one request

Qwen3.7 Max has no comparable published API token rate. Qwen3.8-Omni-Flash has no comparable published API token rate.

Cache-heavy agent loop

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

Qwen3.7 Max: API rate not published; Fits in one request; Cached-input rate unavailable
Qwen3.8-Omni-Flash: API rate not published; Fits in one request; Cached-input rate unavailable

Qwen3.7 Max has no comparable published API token 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.

SpecificationQwen3.7 MaxQwen3.8-Omni-Flash

Context window

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

Qwen3.7 Max

Qwen3.8-Omni-Flash

Alibaba Cloud Model Studio pricing

API model ID

Qwen3.7 Max

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.

Qwen3.7 Max

No comparable hosted API rate

Qwen3.8-Omni-Flash

No comparable hosted API rate

Alibaba Cloud Model Studio pricing

Documented inputs

Qwen3.7 Max

Not sourced

Qwen3.8-Omni-Flash

Not sourced

Documented outputs

Qwen3.7 Max

Not sourced

Qwen3.8-Omni-Flash

Not sourced

Provider availability

Qwen3.7 Max

Not sourced

Qwen3.8-Omni-Flash

Not sourced

Reasoning profile

Qwen3.7 Max

Reasoning

Qwen3.8-Omni-Flash

Reasoning

Weight access

Qwen3.7 Max

Proprietary

Qwen3.8-Omni-Flash

Proprietary

License

Qwen3.7 Max

Proprietary

Qwen3.8-Omni-Flash

Proprietary

Release date

Qwen3.7 Max

2026-05-16

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: 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 evidence53 rows

Agentic

Terminal-Bench 2.0
Qwen3.7 Max69.7%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
QwenClawBench
Qwen3.7 Max64.3%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
Claw-Eval
Qwen3.7 Max65.2%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
BFCL v4
Qwen3.7 Max75.0%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
MCP Atlas
Qwen3.7 Max76.4%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
VITA-Bench
Qwen3.7 Max47.9%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
HLE w/ tools
Qwen3.7 Max53.5%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
Gert Labs
Qwen3.7 Max64.27%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
ResearchClawBench
Qwen3.7 Max18.7%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
Terminal-Bench 2.1 (Vals)
Qwen3.7 Max61.0%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
CoWorkBench
Qwen3.7 Max—
Qwen3.8-Omni-Flash75.3%
Source
Not directly comparable
AndroidWorld
Qwen3.7 Max—
Qwen3.8-Omni-Flash87.1%
Source
Not directly comparable

Coding

SWE-bench Verified
Qwen3.7 Max80.4%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
SWE-bench Pro
Qwen3.7 Max60.6%
Source
Qwen3.8-Omni-Flash63.3%
Source
Qwen3.8-Omni-Flash leads this result
SWE Multilingual
Qwen3.7 Max78.3%
Source
Qwen3.8-Omni-Flash80.5%
Source
Qwen3.8-Omni-Flash leads this result
NL2Repo
Qwen3.7 Max47.2%
Source
Qwen3.8-Omni-Flash48.9%
Source
Qwen3.8-Omni-Flash leads this result
SciCode
Qwen3.7 Max53.5%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
LiveCodeBench
Qwen3.7 Max91.6%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
Terminal-Bench 2.0
Qwen3.7 Max69.7%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
OpenHarmony Bench
Qwen3.7 Max53.4%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
LiveCodeBench (Vals)
Qwen3.7 Max87.1%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
SWE-bench (Vals)
Qwen3.7 Max68.8%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
DeepSWE
Qwen3.7 Max—
Qwen3.8-Omni-Flash57.8%
Source
Not directly comparable
LiveCodeBench v6
Qwen3.7 Max—
Qwen3.8-Omni-Flash92.6%
Source
Not directly comparable

Reasoning

MRCRv2
Qwen3.7 Max90.4%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
CritPt
Qwen3.7 Max13.4%
Source
Qwen3.8-Omni-Flash—
Not directly comparable

Knowledge

GPQA
Qwen3.7 Max92.4%
Source
Qwen3.8-Omni-Flash91%
Source
Qwen3.7 Max leads this result
GPQA-D
Qwen3.7 Max92.4%
Source
Qwen3.8-Omni-Flash91.0%
Source
Qwen3.7 Max leads this result
HLE
Qwen3.7 Max41.4%
Source
Qwen3.8-Omni-Flash36.5%
Source
Qwen3.7 Max leads this result
MMLU-Pro
Qwen3.7 Max89.6%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
MMLU-Redux
Qwen3.7 Max95%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
SuperGPQA
Qwen3.7 Max73.6%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
MMMLU
Qwen3.7 Max90.3%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
GPQA Diamond (Vals)
Qwen3.7 Max90.2%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
MMLU-Pro (Vals)
Qwen3.7 Max89.3%
Source
Qwen3.8-Omni-Flash—
Not directly comparable

Math

HMMT Feb 2026
Qwen3.7 Max97.1%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
IMOAnswerBench
Qwen3.7 Max90.0%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
Apex
Qwen3.7 Max44.5%
Source
Qwen3.8-Omni-Flash—
Not directly comparable

Multilingual

MMLU-ProX
Qwen3.7 Max87%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
NOVA-63
Qwen3.7 Max59.0%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
INCLUDE
Qwen3.7 Max86.2%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
MAXIFE
Qwen3.7 Max89.2%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
PolyMath
Qwen3.7 Max86.5%
Source
Qwen3.8-Omni-Flash—
Not directly comparable

Multimodal

Vision2Web
Qwen3.7 Max—
Qwen3.8-Omni-Flash62.9%
Source
Not directly comparable
ERQA
Qwen3.7 Max—
Qwen3.8-Omni-Flash71.0%
Source
Not directly comparable
LVBench
Qwen3.7 Max—
Qwen3.8-Omni-Flash76.9%
Source
Not directly comparable
RealWorldQA
Qwen3.7 Max—
Qwen3.8-Omni-Flash87.7%
Source
Not directly comparable
MathVision
Qwen3.7 Max—
Qwen3.8-Omni-Flash91.8%
Source
Not directly comparable
MathVision w/ Python
Qwen3.7 Max—
Qwen3.8-Omni-Flash96.2%
Source
Not directly comparable
CharXiv w/o tools
Qwen3.7 Max—
Qwen3.8-Omni-Flash83.5%
Source
Not directly comparable
CharXiv
Qwen3.7 Max—
Qwen3.8-Omni-Flash91.4%
Source
Not directly comparable

Instruction following

IFEval
Qwen3.7 Max94.3%
Source
Qwen3.8-Omni-Flash—
Not directly comparable
IFBench
Qwen3.7 Max79.1%
Source
Qwen3.8-Omni-Flash81.5%
Source
Qwen3.8-Omni-Flash leads this result

Questions

Which is better, Qwen3.7 Max 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, Qwen3.7 Max or Qwen3.8-Omni-Flash?

Qwen3.8-Omni-Flash scores higher for coding on the public lane, 53.5 to 49.2. Qwen3.8-Omni-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, Qwen3.7 Max 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, Qwen3.7 Max 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, Qwen3.7 Max or Qwen3.8-Omni-Flash?

Both models list the same context window, 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 18, 2026

Watch Qwen3.7 Max vs Qwen3.8-Omni-Flash

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

Read a sample issue

Join 2,000+ readers.

Qwen3.7 Max vs Qwen3.8-Omni-Flash

Which one for your work

Coding work

Agentic work

Long documents

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

What is actually comparable

Category results, on a stated basis

Instruction following

Coding

Knowledge

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, Qwen3.7 Max or Qwen3.8-Omni-Flash?

Which is better for coding, Qwen3.7 Max or Qwen3.8-Omni-Flash?

Which is better for agentic tasks, Qwen3.7 Max or Qwen3.8-Omni-Flash?

Which costs less, Qwen3.7 Max or Qwen3.8-Omni-Flash?

Which has the larger context window, Qwen3.7 Max or Qwen3.8-Omni-Flash?

Related comparisons

Watch Qwen3.7 Max vs Qwen3.8-Omni-Flash