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BenchLM
DeepSeek logo
Model A
DeepSeek V3

DeepSeek

44.5/100

Supported · Public rank #172

90% interval 25.4–63.5

DeepSeek V3 vs Qwen3.8-Flash-Next

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

Alibaba logo
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 44.46, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

1 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.

  • Long documents

    Prompts that approach the documented context limit

    Qwen3.8-Flash-Next

    Qwen3.8-Flash-Next 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

    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

  • 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. DeepSeek V3 does not fit this workload in one request. Qwen3.8-Flash-Next has no comparable published API token rate.

    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.

Shared results
1
DeepSeek V3 only
5
Qwen3.8-Flash-Next only
23
Like-for-like categories
0 / 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.

Knowledge

Directional only
DeepSeek V3
72.7
Qwen3.8-Flash-Next
43.4
Weighted basis
2 vs 2 rows
Reading
Directional only

Agentic

Not comparable
DeepSeek V3
Not measured
Qwen3.8-Flash-Next
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Coding

Not comparable
DeepSeek V3
38.9
Qwen3.8-Flash-Next
62.5
Weighted basis
2 vs 1 rows
Reading
Not comparable

Reasoning

Not comparable
DeepSeek V3
Not measured
Qwen3.8-Flash-Next
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
DeepSeek V3
1.7
Qwen3.8-Flash-Next
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
DeepSeek V3
Not measured
Qwen3.8-Flash-Next
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
DeepSeek V3
Not measured
Qwen3.8-Flash-Next
90.6
Weighted basis
0 vs 1 rows
Reading
Not comparable

Instruction following

Not comparable
DeepSeek V3
86.1
Qwen3.8-Flash-Next
81.3
Weighted basis
1 vs 1 rows
Reading
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.

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

DeepSeek V3
$0.00082
Fits in one request
Qwen3.8-Flash-Next
Self-hosted; infrastructure cost varies
Fits in one request

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

Repository review

50K fresh input + 3K output tokens

DeepSeek V3
$0.0168
Fits in one request
Qwen3.8-Flash-Next
Self-hosted; infrastructure cost varies
Fits in one request

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

Cache-heavy agent loop

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

DeepSeek V3
$0.0304
Does not fit in one request
Qwen3.8-Flash-Next
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

DeepSeek V3 does not fit this workload in one request. 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.

Cached-input rate

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

DeepSeek V3

$0.07 per 1M cached input tokens

Qwen3.8-Flash-Next

No comparable hosted API rate

Qwen3.8-Flash-Next model card

Documented inputs

DeepSeek V3

Not sourced

Qwen3.8-Flash-Next

Not sourced

Documented outputs

DeepSeek V3

Not sourced

Qwen3.8-Flash-Next

Not sourced

Provider availability

DeepSeek V3

Not sourced

Qwen3.8-Flash-Next

Not sourced

Reasoning profile

DeepSeek V3

Non-Reasoning

Qwen3.8-Flash-Next

Reasoning

Weight access

DeepSeek V3

Open Weight

Qwen3.8-Flash-Next

Open Weight

License

DeepSeek V3

Open Weight

Qwen3.8-Flash-Next

Open Weight

Release date

DeepSeek V3

2024-12-26

Qwen3.8-Flash-Next

2026-08-26

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
Qwen3.8-Flash-Next has the higher public score estimate, 67.54 versus 44.46, but the 90% score intervals overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Qwen3.8-Flash-Next has the larger documented window (262K).

Run the same representative tasks against both endpoints before changing production traffic.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

DeepSeek V3
API / mo$1,028
Self-host / mo$18,221
Break-even1.2B/day
Qwen3.8-Flash-Next
API / mo$0
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Model the full break-even

Benchmark evidence

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

Browse raw public benchmark evidence29 rows

Agentic

  • CoWorkBench

    DeepSeek V3—
    Qwen3.8-Flash-Next73.9%
    Source

    Not directly comparable

  • JobBench

    DeepSeek V3—
    Qwen3.8-Flash-Next55.7%
    Source

    Not directly comparable

  • Agents' Last Exam

    DeepSeek V3—
    Qwen3.8-Flash-Next51.2%
    Source

    Not directly comparable

  • Toolathlon-Verified

    DeepSeek V3—
    Qwen3.8-Flash-Next73.5%
    Source

    Not directly comparable

  • AndroidWorld

    DeepSeek V3—
    Qwen3.8-Flash-Next84.5%
    Source

    Not directly comparable

  • OSWorld 2.0

    DeepSeek V3—
    Qwen3.8-Flash-Next19.4%
    Source

    Not directly comparable

Coding

  • LiveCodeBench

    DeepSeek V337.6%
    Source
    Qwen3.8-Flash-Next—

    Not directly comparable

  • SWE-bench Verified

    DeepSeek V342%
    Source
    Qwen3.8-Flash-Next—

    Not directly comparable

  • SWE-bench Pro

    DeepSeek V3—
    Qwen3.8-Flash-Next62.5%
    Source

    Not directly comparable

  • SWE Multilingual

    DeepSeek V3—
    Qwen3.8-Flash-Next81%
    Source

    Not directly comparable

  • NL2Repo

    DeepSeek V3—
    Qwen3.8-Flash-Next48.1%
    Source

    Not directly comparable

  • deepSwe

    DeepSeek V3—
    Qwen3.8-Flash-Next58.7%
    Source

    Not directly comparable

  • LiveCodeBench v6

    DeepSeek V3—
    Qwen3.8-Flash-Next91.9%
    Source

    Not directly comparable

Knowledge

  • GPQA

    DeepSeek V359.1%
    Source
    Qwen3.8-Flash-Next91.7%
    Source

    Qwen3.8-Flash-Next leads this result

  • MMLU-Pro

    DeepSeek V375.9%
    Source
    Qwen3.8-Flash-Next—

    Not directly comparable

  • GPQA-D

    DeepSeek V3—
    Qwen3.8-Flash-Next91.7%
    Source

    Not directly comparable

  • HLE

    DeepSeek V3—
    Qwen3.8-Flash-Next35.9%
    Source

    Not directly comparable

  • HLE w/o tools

    DeepSeek V3—
    Qwen3.8-Flash-Next35.9%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    DeepSeek V31.724%
    Source
    Qwen3.8-Flash-Next—

    Not directly comparable

Multimodal

  • Vision2Web

    DeepSeek V3—
    Qwen3.8-Flash-Next64.0%
    Source

    Not directly comparable

  • ERQA

    DeepSeek V3—
    Qwen3.8-Flash-Next72.3%
    Source

    Not directly comparable

  • LVBench

    DeepSeek V3—
    Qwen3.8-Flash-Next76.6%
    Source

    Not directly comparable

  • RealWorldQA

    DeepSeek V3—
    Qwen3.8-Flash-Next88.5%
    Source

    Not directly comparable

  • MathVision

    DeepSeek V3—
    Qwen3.8-Flash-Next90.6%
    Source

    Not directly comparable

  • MathVision w/ Python

    DeepSeek V3—
    Qwen3.8-Flash-Next95.7%
    Source

    Not directly comparable

  • CharXiv w/o tools

    DeepSeek V3—
    Qwen3.8-Flash-Next84.6%
    Source

    Not directly comparable

  • CharXiv

    DeepSeek V3—
    Qwen3.8-Flash-Next90.6%
    Source

    Not directly comparable

Instruction following

  • IFEval

    DeepSeek V386.1%
    Source
    Qwen3.8-Flash-Next—

    Not directly comparable

  • IFBench

    DeepSeek V3—
    Qwen3.8-Flash-Next81.3%
    Source

    Not directly comparable

Frequently asked questions

Which is better, DeepSeek V3 or Qwen3.8-Flash-Next?

Qwen3.8-Flash-Next has the higher public score estimate, 67.54 versus 44.46, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, DeepSeek V3 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, DeepSeek V3 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, DeepSeek V3 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, DeepSeek V3 or Qwen3.8-Flash-Next?

Qwen3.8-Flash-Next has the larger documented context window: 262K, compared with 128K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 26, 2026

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