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BenchLM

DeepSeek V4 Pro 0813 vs Qwen3.5 Flash

Updated September 27, 2026. Rank says DeepSeek V4 Pro 0813 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

DeepSeek V4 Pro 0813 has the higher public score estimate, 63.48 versus 45.49, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 4 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
DeepSeek logo
DeepSeek V4 Pro 0813

DeepSeek

63.48/100

Estimated · Public rank #33

90% interval 52.0–75.0

Model B
Alibaba logo
Qwen3.5 Flash

Alibaba

45.49/100

Estimated · Public rank #93

90% interval 36.4–54.6

Shared results
4
DeepSeek V4 Pro 0813 only
38
Qwen3.5 Flash only
2
Like-for-like categories
0 / 8
Estimated: DeepSeek V4 Pro 0813 and Qwen3.5 FlashHow the comparison works

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

    Qwen3.5 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

    Qwen3.5 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

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.

50.3DeepSeek V4 Pro 081326.5Qwen3.5 Flash

Directional only · BenchAlign v5.7

DeepSeek V4 Pro 0813 scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.

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

Open the coding leaderboardShortlist models for this job
Same basis rules as the category table below

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

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

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.

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.7 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
DeepSeek V4 Pro 0813
50.3
Supported · #39/135
Qwen3.5 Flash
26.5
Estimated · #97/135
Basis
BenchAlign v5.7 lane · 15 vs 2 public rows
Reading
Directional only

Knowledge

Directional only
DeepSeek V4 Pro 0813
63.4
Estimated · #29/158
Qwen3.5 Flash
43.0
Estimated · #80/158
Basis
BenchAlign v5.7 lane · 8 vs 2 public rows
Reading
Directional only

Agentic

Not comparable
DeepSeek V4 Pro 0813
55.0
Supported · #29/105
Qwen3.5 Flash
Not ranked
Basis
BenchAlign v5.7 lane · 11 vs 0 public rows
Reading
Not comparable

Reasoning

Not comparable
DeepSeek V4 Pro 0813
56.9
Unranked · 4 rankable rows
Qwen3.5 Flash
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
DeepSeek V4 Pro 0813
Not ranked
Qwen3.5 Flash
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
DeepSeek V4 Pro 0813
Not ranked
Qwen3.5 Flash
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
DeepSeek V4 Pro 0813
Not ranked
Qwen3.5 Flash
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
DeepSeek V4 Pro 0813
80.2
Unranked · 4 rankable rows
Qwen3.5 Flash
28.4
Unranked · 2 rankable rows
Basis
Provisional lane · 1 vs 2 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

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 V4 Pro 0813
$0.0033
Fits in one request
Qwen3.5 Flash
$0.0003
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

DeepSeek V4 Pro 0813
$0.07788
Fits in one request
Qwen3.5 Flash
$0.0062
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

DeepSeek V4 Pro 0813
$0.0748
Fits in one request
Qwen3.5 Flash
$0.026
Fits in one request
Cached input priced at the published list-input rate

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.

Cached input falls back to the list input rate only where a cached rate is unpublished

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

Context window

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

DeepSeek V4 Pro 0813

1M

DeepSeek models and pricing

Qwen3.5 Flash

1M

Cached-input rate

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

DeepSeek V4 Pro 0813

$0.044 per 1M cached input tokens

DeepSeek: Models & Pricing

Qwen3.5 Flash

Not published

Reasoning profile

DeepSeek V4 Pro 0813

Reasoning

Qwen3.5 Flash

Reasoning

Weight access

DeepSeek V4 Pro 0813

Open Weight

Qwen3.5 Flash

Proprietary

License

DeepSeek V4 Pro 0813

Open Weight

Qwen3.5 Flash

Proprietary

Release date

DeepSeek V4 Pro 0813

2026-08-13

Qwen3.5 Flash

2026-03-04

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
DeepSeek V4 Pro 0813 has the higher public score estimate, 63.48 versus 45.49, but the 90% score intervals overlap.
Workload cost
Repository review: $0.07788 vs $0.0062. Cache-heavy agent loop: $0.0748 vs $0.026.
Context tradeoff
Both models list 1M.
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, DeepSeek V4 Pro 0813 or Qwen3.5 Flash?

DeepSeek V4 Pro 0813 has the higher public score estimate, 63.48 versus 45.49, 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 V4 Pro 0813 or Qwen3.5 Flash?

DeepSeek V4 Pro 0813 scores higher for coding on the public lane, 50.3 to 26.5. Qwen3.5 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, DeepSeek V4 Pro 0813 or Qwen3.5 Flash?

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

Which costs less, DeepSeek V4 Pro 0813 or Qwen3.5 Flash?

For the stated presets, chat costs $0.0033 on DeepSeek V4 Pro 0813 and $0.0003 on Qwen3.5 Flash; repository review costs $0.07788 and $0.0062; the cache-heavy agent loop costs $0.0748 and $0.026. Qwen3.5 Flash has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, DeepSeek V4 Pro 0813 or Qwen3.5 Flash?

Both models list the same context window, 1M.

Benchmark evidence

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

Browse raw public benchmark evidence44 rows

Agentic

  • Terminal-Bench 2.0

    DeepSeek V4 Pro 081367.9%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • Terminal-Bench 2.1

    DeepSeek V4 Pro 081387.9%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • BrowseComp

    DeepSeek V4 Pro 081383.4%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • HLE w/ tools

    DeepSeek V4 Pro 081360.0%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • MCP Atlas

    DeepSeek V4 Pro 081373.6%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • Toolathlon

    DeepSeek V4 Pro 081351.8%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • CyberGym

    DeepSeek V4 Pro 081383.3%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • Toolathlon-Verified

    DeepSeek V4 Pro 081374.1%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • Agents' Last Exam

    DeepSeek V4 Pro 081325.7%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • AutomationBench

    DeepSeek V4 Pro 081331.8%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    DeepSeek V4 Pro 081354.7%
    Source
    Qwen3.5 Flash—

    Not directly comparable

Coding

  • LiveCodeBench Pass@1-COT

    DeepSeek V4 Pro 081393.5%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • Codeforces

    DeepSeek V4 Pro 08133206.0
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • SWE-bench Verified

    DeepSeek V4 Pro 081380.6%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • SWE-bench Pro

    DeepSeek V4 Pro 081355.4%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • SWE Multilingual

    DeepSeek V4 Pro 081376.2%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • Terminal-Bench 2.0

    DeepSeek V4 Pro 081367.9%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • Vibe Code Bench

    DeepSeek V4 Pro 081349.93%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • Terminal-Bench 2.1

    DeepSeek V4 Pro 081387.9%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • NL2Repo

    DeepSeek V4 Pro 081361.5%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • DeepSWE

    DeepSeek V4 Pro 081362.7%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • DSBench-FullStack

    DeepSeek V4 Pro 081371.1%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • DSBench-Hard

    DeepSeek V4 Pro 081367.2%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • OpenHarmony Bench

    DeepSeek V4 Pro 081359.0%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • LiveCodeBench (Vals)

    DeepSeek V4 Pro 081387.5%
    Source
    Qwen3.5 Flash83.3%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • SWE-bench (Vals)

    DeepSeek V4 Pro 081396.4%
    Source
    Qwen3.5 Flash64.4%
    Source

    DeepSeek V4 Pro 0813 leads this result

Reasoning

  • MRCR 1M

    DeepSeek V4 Pro 081383.5%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • CorpusQA 1M

    DeepSeek V4 Pro 081362.0%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • ARC-AGI-1

    DeepSeek V4 Pro 081390.00%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • ARC-AGI-2

    DeepSeek V4 Pro 081361.3%
    Source
    Qwen3.5 Flash—

    Not directly comparable

Knowledge

  • MMLU-Pro

    DeepSeek V4 Pro 081387.5%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • SimpleQA

    DeepSeek V4 Pro 081357.9%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • Chinese-SimpleQA

    DeepSeek V4 Pro 081384.4%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • GPQA

    DeepSeek V4 Pro 081390.1%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • GPQA-D

    DeepSeek V4 Pro 081390.1%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • HLE

    DeepSeek V4 Pro 081342.7%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • GPQA Diamond (Vals)

    DeepSeek V4 Pro 081392.4%
    Source
    Qwen3.5 Flash82.8%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • MMLU-Pro (Vals)

    DeepSeek V4 Pro 081387.0%
    Source
    Qwen3.5 Flash84.1%
    Source

    DeepSeek V4 Pro 0813 leads this result

Math

  • HMMT Feb 2026

    DeepSeek V4 Pro 081395.2%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • IMOAnswerBench

    DeepSeek V4 Pro 081389.8%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • Apex

    DeepSeek V4 Pro 081338.3%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • Apex Shortlist

    DeepSeek V4 Pro 081390.2%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    DeepSeek V4 Pro 0813—
    Qwen3.5 Flash6.207%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    DeepSeek V4 Pro 0813—
    Qwen3.5 Flash0.000%
    Source

    Not directly comparable

44 public results · 4 shared

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Last updated September 27, 2026