Model A

Alibaba

59.7/100

Supported · Public rank #71

90% interval 48.2–71.2

Qwen3.5-122B-A10B vs Qwen3.8-Flash-Next

Updated August 26, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B

Qwen3.8-Flash-Next

Alibaba

67.5/100

Estimated · Public rank #25

90% interval 57.7–77.4

Decision reading

Qwen3.8-Flash-Next has the higher public score estimate, 67.54 versus 59.73, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

3 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.

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
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
No shared weighted benchmark basis supports a winner.
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.

12 Qwen3.5-122B-A10B only

3 Shared

21 Qwen3.8-Flash-Next only

Shared results: 3
Qwen3.5-122B-A10B only: 12
Qwen3.8-Flash-Next only: 21
Like-for-like categories: 1 / 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.

Multimodal

Like-for-like

Qwen3.5-122B-A10B: 77.2
Qwen3.8-Flash-Next: 90.6
Weighted basis: 1 vs 1 rows
Reading: Qwen3.8-Flash-Next leads

Knowledge

Directional only

Qwen3.5-122B-A10B: 83.6
Qwen3.8-Flash-Next: 43.4
Weighted basis: 3 vs 2 rows
Reading: Directional only

Agentic

Not comparable

Qwen3.5-122B-A10B: 56.4
Qwen3.8-Flash-Next: Not measured
Weighted basis: 3 vs 0 rows
Reading: Not comparable

Coding

Not comparable

Qwen3.5-122B-A10B: 72.0
Qwen3.8-Flash-Next: 62.5
Weighted basis: 1 vs 1 rows
Reading: Not comparable

Reasoning

Not comparable

Qwen3.5-122B-A10B: 60.2
Qwen3.8-Flash-Next: Not measured
Weighted basis: 1 vs 0 rows
Reading: Not comparable

Math

Not comparable

Qwen3.5-122B-A10B: Not measured
Qwen3.8-Flash-Next: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Multilingual

Not comparable

Qwen3.5-122B-A10B: 82.2
Qwen3.8-Flash-Next: Not measured
Weighted basis: 1 vs 0 rows
Reading: Not comparable

Instruction following

Not comparable

Qwen3.5-122B-A10B: 93.4
Qwen3.8-Flash-Next: 81.3
Weighted basis: 1 vs 1 rows
Reading: Not comparable

Category averages with the server-provided evidence basis for Qwen3.5-122B-A10B and Qwen3.8-Flash-Next
Category	Qwen3.5-122B-A10B	Qwen3.8-Flash-Next	Weighted basis	Reading
Multimodal	77.2	90.6	Like-for-like1 vs 1 rows	Qwen3.8-Flash-Next leads
Knowledge	83.6	43.4	Directional only3 vs 2 rows	Directional only
Agentic	56.4	Not measured	Not comparable3 vs 0 rows	Not comparable
Coding	72.0	62.5	Not comparable1 vs 1 rows	Not comparable
Reasoning	60.2	Not measured	Not comparable1 vs 0 rows	Not comparable
Math	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable
Multilingual	82.2	Not measured	Not comparable1 vs 0 rows	Not comparable
Instruction following	93.4	81.3	Not comparable1 vs 1 rows	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.

CharXiv
Multimodal
Qwen3.5-122B-A10B: 77.2%Qwen3.8-Flash-Next: 90.6%Normalized gap 13.4Qwen3.5-122B-A10B source Qwen3.8-Flash-Next source
GPQA
Knowledge
Qwen3.5-122B-A10B: 86.6%Qwen3.8-Flash-Next: 91.7%Normalized gap 5.1Qwen3.5-122B-A10B source Qwen3.8-Flash-Next 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

Qwen3.5-122B-A10B: Self-hosted; infrastructure cost varies; Fits in one request
Qwen3.8-Flash-Next: Self-hosted; infrastructure cost varies; Fits in one request

Qwen3.5-122B-A10B has no comparable published API token rate. Qwen3.8-Flash-Next has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Qwen3.5-122B-A10B: Self-hosted; infrastructure cost varies; Fits in one request
Qwen3.8-Flash-Next: Self-hosted; infrastructure cost varies; Fits in one request

Qwen3.5-122B-A10B has no comparable published API token rate. Qwen3.8-Flash-Next has no comparable published API token rate.

Cache-heavy agent loop

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

Qwen3.5-122B-A10B: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable
Qwen3.8-Flash-Next: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable

Qwen3.5-122B-A10B has no comparable published API token rate. Qwen3.8-Flash-Next 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.

SpecificationQwen3.5-122B-A10BQwen3.8-Flash-Next

Context window

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

Qwen3.5-122B-A10B

262K

Qwen3.8-Flash-Next

262K

Qwen3.8-Flash-Next model card

API model ID

Qwen3.5-122B-A10B

Not sourced

Qwen3.8-Flash-Next

Qwen/Qwen3.8-Flash-Next

Qwen3.8-Flash-Next model card

Cached-input rate

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

Qwen3.5-122B-A10B

No comparable hosted API rate

Qwen3.8-Flash-Next

No comparable hosted API rate

Qwen3.8-Flash-Next model card

Documented inputs

Qwen3.5-122B-A10B

Not sourced

Qwen3.8-Flash-Next

Not sourced

Documented outputs

Qwen3.5-122B-A10B

Not sourced

Qwen3.8-Flash-Next

Not sourced

Provider availability

Qwen3.5-122B-A10B

Not sourced

Qwen3.8-Flash-Next

Not sourced

Reasoning profile

Qwen3.5-122B-A10B

Reasoning

Qwen3.8-Flash-Next

Reasoning

Weight access

Qwen3.5-122B-A10B

Open Weight

Qwen3.8-Flash-Next

Open Weight

License

Qwen3.5-122B-A10B

Open Weight

Qwen3.8-Flash-Next

Open Weight

Release date

Qwen3.5-122B-A10B

2026-03-04

Qwen3.8-Flash-Next

2026-08-26

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: Qwen3.8-Flash-Next has the higher public score estimate, 67.54 versus 59.73, but the 90% score intervals overlap.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Both models list 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 evidence36 rows

Agentic

Terminal-Bench 2.0
Qwen3.5-122B-A10B49.4%
Source
Qwen3.8-Flash-Next—
Not directly comparable
BrowseComp
Qwen3.5-122B-A10B63.8%
Source
Qwen3.8-Flash-Next—
Not directly comparable
OSWorld-Verified
Qwen3.5-122B-A10B58%
Source
Qwen3.8-Flash-Next—
Not directly comparable
CoWorkBench
Qwen3.5-122B-A10B—
Qwen3.8-Flash-Next73.9%
Source
Not directly comparable
JobBench
Qwen3.5-122B-A10B—
Qwen3.8-Flash-Next55.7%
Source
Not directly comparable
Agents' Last Exam
Qwen3.5-122B-A10B—
Qwen3.8-Flash-Next51.2%
Source
Not directly comparable
Toolathlon-Verified
Qwen3.5-122B-A10B—
Qwen3.8-Flash-Next73.5%
Source
Not directly comparable
AndroidWorld
Qwen3.5-122B-A10B—
Qwen3.8-Flash-Next84.5%
Source
Not directly comparable
OSWorld 2.0
Qwen3.5-122B-A10B—
Qwen3.8-Flash-Next19.4%
Source
Not directly comparable

Coding

SWE-bench Verified
Qwen3.5-122B-A10B72%
Source
Qwen3.8-Flash-Next—
Not directly comparable
SWE-bench Pro
Qwen3.5-122B-A10B—
Qwen3.8-Flash-Next62.5%
Source
Not directly comparable
SWE Multilingual
Qwen3.5-122B-A10B—
Qwen3.8-Flash-Next81%
Source
Not directly comparable
NL2Repo
Qwen3.5-122B-A10B—
Qwen3.8-Flash-Next48.1%
Source
Not directly comparable
deepSwe
Qwen3.5-122B-A10B—
Qwen3.8-Flash-Next58.7%
Source
Not directly comparable
LiveCodeBench v6
Qwen3.5-122B-A10B—
Qwen3.8-Flash-Next91.9%
Source
Not directly comparable

Reasoning

LongBench v2
Qwen3.5-122B-A10B60.2%
Source
Qwen3.8-Flash-Next—
Not directly comparable

Knowledge

MMLU-Pro
Qwen3.5-122B-A10B86.7%
Source
Qwen3.8-Flash-Next—
Not directly comparable
SuperGPQA
Qwen3.5-122B-A10B67.1%
Source
Qwen3.8-Flash-Next—
Not directly comparable
GPQA
Qwen3.5-122B-A10B86.6%
Source
Qwen3.8-Flash-Next91.7%
Source
Qwen3.8-Flash-Next leads this result
GPQA-D
Qwen3.5-122B-A10B—
Qwen3.8-Flash-Next91.7%
Source
Not directly comparable
HLE
Qwen3.5-122B-A10B—
Qwen3.8-Flash-Next35.9%
Source
Not directly comparable
HLE w/o tools
Qwen3.5-122B-A10B—
Qwen3.8-Flash-Next35.9%
Source
Not directly comparable

Multilingual

MMLU-ProX
Qwen3.5-122B-A10B82.2%
Source
Qwen3.8-Flash-Next—
Not directly comparable

Multimodal

MMMU
Qwen3.5-122B-A10B83.9%
Source
Qwen3.8-Flash-Next—
Not directly comparable
MMVU
Qwen3.5-122B-A10B74.7%
Source
Qwen3.8-Flash-Next—
Not directly comparable
MathVision
Qwen3.5-122B-A10B86.2%
Source
Qwen3.8-Flash-Next90.6%
Source
Qwen3.8-Flash-Next leads this result
CharXiv
Qwen3.5-122B-A10B77.2%
Source
Qwen3.8-Flash-Next90.6%
Source
Qwen3.8-Flash-Next leads this result
V*
Qwen3.5-122B-A10B93.2%
Source
Qwen3.8-Flash-Next—
Not directly comparable
Vision2Web
Qwen3.5-122B-A10B—
Qwen3.8-Flash-Next64.0%
Source
Not directly comparable
ERQA
Qwen3.5-122B-A10B—
Qwen3.8-Flash-Next72.3%
Source
Not directly comparable
LVBench
Qwen3.5-122B-A10B—
Qwen3.8-Flash-Next76.6%
Source
Not directly comparable
RealWorldQA
Qwen3.5-122B-A10B—
Qwen3.8-Flash-Next88.5%
Source
Not directly comparable
MathVision w/ Python
Qwen3.5-122B-A10B—
Qwen3.8-Flash-Next95.7%
Source
Not directly comparable
CharXiv w/o tools
Qwen3.5-122B-A10B—
Qwen3.8-Flash-Next84.6%
Source
Not directly comparable

Instruction following

IFEval
Qwen3.5-122B-A10B93.4%
Source
Qwen3.8-Flash-Next—
Not directly comparable
IFBench
Qwen3.5-122B-A10B—
Qwen3.8-Flash-Next81.3%
Source
Not directly comparable

Frequently asked questions

Which is better, Qwen3.5-122B-A10B or Qwen3.8-Flash-Next?

Qwen3.8-Flash-Next has the higher public score estimate, 67.54 versus 59.73, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Qwen3.5-122B-A10B or Qwen3.8-Flash-Next?

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, Qwen3.5-122B-A10B or Qwen3.8-Flash-Next?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, Qwen3.5-122B-A10B or Qwen3.8-Flash-Next?

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, Qwen3.5-122B-A10B or Qwen3.8-Flash-Next?

Both models list the same context window, 262K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 26, 2026

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Qwen3.5-122B-A10B vs Qwen3.8-Flash-Next

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

Multimodal

Knowledge

Agentic

Coding

Reasoning

Math

Multilingual

Instruction following

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

Multilingual

Multimodal

Instruction following

Frequently asked questions

Which is better, Qwen3.5-122B-A10B or Qwen3.8-Flash-Next?

Which is better for coding, Qwen3.5-122B-A10B or Qwen3.8-Flash-Next?

Which is better for agentic tasks, Qwen3.5-122B-A10B or Qwen3.8-Flash-Next?

Which costs less, Qwen3.5-122B-A10B or Qwen3.8-Flash-Next?

Which has the larger context window, Qwen3.5-122B-A10B or Qwen3.8-Flash-Next?

Related comparisons

Watch Qwen3.5-122B-A10B vs Qwen3.8-Flash-Next