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DeepSeek V3 vs DeepSeek V4.1 Flash 1017

Updated October 25, 2026. Rank says DeepSeek V4.1 Flash 1017 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 V4.1 Flash 1017 has the higher public score, 65.78 versus 31.89, and the 90% score intervals do not overlap. 1 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 V3

DeepSeek

31.89/100

Supported · Public rank #174

90% interval 15.5–48.3

Model B
DeepSeek logo
DeepSeek V4.1 Flash 1017

DeepSeek

65.78/100

Supported · Public rank #35

90% interval 60.8–70.8

Shared results
1
DeepSeek V3 only
8
DeepSeek V4.1 Flash 1017 only
32
Like-for-like categories
1 / 8
Supported: DeepSeek V3 and DeepSeek V4.1 Flash 1017How 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.

  • Long documents

    Prompts that approach the documented context limit

    DeepSeek V4.1 Flash 1017

    DeepSeek V4.1 Flash 1017 has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    DeepSeek V3

    DeepSeek V3 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates
Show secondary and unsupported calls

  • Repository review cost

    50K fresh input + 3K output tokens

    DeepSeek V3

    DeepSeek V3 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

    DeepSeek V3 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

    DeepSeek V3 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    Confidence: limited

  • 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.

    Confidence: listed-rates

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.

14.9DeepSeek V361.1DeepSeek V4.1 Flash 1017

Directional only · BenchAlign v5.8

DeepSeek V4.1 Flash 1017 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

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 v5.8 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.

Knowledge

Like-for-like
DeepSeek V3
31.4
Supported · #149/187
DeepSeek V4.1 Flash 1017
64.4
Supported · #37/187
Basis
BenchAlign v5.8 lane · 3 vs 3 public rows
Reading
DeepSeek V4.1 Flash 1017 leads

Agentic

Directional only
DeepSeek V3
10.8
Estimated · #126/132
DeepSeek V4.1 Flash 1017
61.7
Supported · #24/132
Basis
BenchAlign v5.8 lane · 0 vs 11 public rows
Reading
Directional only

Coding

Directional only
DeepSeek V3
14.9
Estimated · #138/153
DeepSeek V4.1 Flash 1017
61.1
Supported · #18/153
Basis
BenchAlign v5.8 lane · 2 vs 8 public rows
Reading
Directional only

Reasoning

Not comparable
DeepSeek V3
25.5
Unranked · 3 rankable rows
DeepSeek V4.1 Flash 1017
82.5
Unranked · 4 rankable rows
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
DeepSeek V3
Not ranked
DeepSeek V4.1 Flash 1017
78.8
Unranked · 1 rankable row
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
DeepSeek V3
Not ranked
DeepSeek V4.1 Flash 1017
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
DeepSeek V3
39.6
#120/153
DeepSeek V4.1 Flash 1017
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
DeepSeek V3
Not ranked
DeepSeek V4.1 Flash 1017
70.1
Unranked · 3 rankable rows
Basis
Provisional lane · 0 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 v5.8) 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 V3
$0.00082
Fits in one request
DeepSeek V4.1 Flash 1017
$0.0009
Fits in one request

DeepSeek V3 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

DeepSeek V3
$0.0168
Fits in one request
DeepSeek V4.1 Flash 1017
$0.0186
Fits in one request

DeepSeek V3 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 V3
$0.0304
Does not fit in one request
DeepSeek V4.1 Flash 1017
$0.0192
Fits in one request

DeepSeek V3 does not fit this workload in one request.

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 V3

128K

DeepSeek V4.1 Flash 1017

1M

DeepSeek models and pricing

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

DeepSeek V4.1 Flash 1017

$0.006 per 1M cached input tokens

DeepSeek: Models & Pricing

Reasoning profile

DeepSeek V3

Non-Reasoning

DeepSeek V4.1 Flash 1017

Reasoning

Weight access

DeepSeek V3

Open Weight

DeepSeek V4.1 Flash 1017

Open Weight

License

DeepSeek V3

Open Weight

DeepSeek V4.1 Flash 1017

Open Weight

Release date

DeepSeek V3

2024-12-26

DeepSeek V4.1 Flash 1017

2026-10-17

If you already use one of these models

Deployment change
Both entries list DeepSeek as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
Quality signal
DeepSeek V4.1 Flash 1017 has the higher public score, 65.78 versus 31.89, and the 90% score intervals do not overlap.
Workload cost
Repository review: $0.0168 vs $0.0186. Cache-heavy agent loop: $0.0304 vs $0.0192.
Context tradeoff
DeepSeek V4.1 Flash 1017 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, DeepSeek V3 or DeepSeek V4.1 Flash 1017?

DeepSeek V4.1 Flash 1017 has the higher public score, 65.78 versus 31.89, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, DeepSeek V3 or DeepSeek V4.1 Flash 1017?

DeepSeek V4.1 Flash 1017 scores higher for coding on the public lane, 61.1 to 14.9. DeepSeek V3 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 V3 or DeepSeek V4.1 Flash 1017?

DeepSeek V4.1 Flash 1017 scores higher for agentic tasks on the public lane, 61.7 to 10.8. DeepSeek V3 is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, DeepSeek V3 or DeepSeek V4.1 Flash 1017?

For the stated presets, chat costs $0.00082 on DeepSeek V3 and $0.0009 on DeepSeek V4.1 Flash 1017; repository review costs $0.0168 and $0.0186; the cache-heavy agent loop costs $0.0304 and $0.0192. DeepSeek V3 does not fit this workload in one request.

Which has the larger context window, DeepSeek V3 or DeepSeek V4.1 Flash 1017?

DeepSeek V4.1 Flash 1017 has the larger documented context window: 1M, compared with 128K.

Self-host vs API cost

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

DeepSeek V3
API / mo$1,028
Self-host / mo$27,962
Break-even1.8B/day
DeepSeek V4.1 Flash 1017
API / mo$1,125
Self-host / mo$27,962
Break-even1.6B/day
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 evidence41 rows

Agentic

  • Terminal-Bench 2.1

    DeepSeek V3—
    DeepSeek V4.1 Flash 101790.6%
    Source

    Not directly comparable

  • terminalBench3

    DeepSeek V3—
    DeepSeek V4.1 Flash 101730%
    Source

    Not directly comparable

  • Terminal-Bench 4.0

    DeepSeek V3—
    DeepSeek V4.1 Flash 101742.30%
    Source

    Not directly comparable

  • CyberGym

    DeepSeek V3—
    DeepSeek V4.1 Flash 101788.1%
    Source

    Not directly comparable

  • ExploitGym

    DeepSeek V3—
    DeepSeek V4.1 Flash 101715.3%
    Source

    Not directly comparable

  • HLE w/ tools

    DeepSeek V3—
    DeepSeek V4.1 Flash 101763.9%
    Source

    Not directly comparable

  • AutomationBench

    DeepSeek V3—
    DeepSeek V4.1 Flash 101760.5%
    Source

    Not directly comparable

  • Agents' Last Exam

    DeepSeek V3—
    DeepSeek V4.1 Flash 101735.4%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    DeepSeek V3—
    DeepSeek V4.1 Flash 101774.5%
    Source

    Not directly comparable

  • CWE-bench v1

    DeepSeek V3—
    DeepSeek V4.1 Flash 101755.0%
    Source

    Not directly comparable

  • MazeBench

    DeepSeek V3—
    DeepSeek V4.1 Flash 10171%
    Source

    Not directly comparable

Coding

  • LiveCodeBench

    DeepSeek V337.6%
    Source
    DeepSeek V4.1 Flash 1017—

    Not directly comparable

  • SWE-bench Verified

    DeepSeek V342%
    Source
    DeepSeek V4.1 Flash 1017—

    Not directly comparable

  • Codeforces

    DeepSeek V3—
    DeepSeek V4.1 Flash 10173471.0
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    DeepSeek V3—
    DeepSeek V4.1 Flash 101790.6%
    Source

    Not directly comparable

  • terminalBench3

    DeepSeek V3—
    DeepSeek V4.1 Flash 101730%
    Source

    Not directly comparable

  • DeepSWE

    DeepSeek V3—
    DeepSeek V4.1 Flash 101777.0%
    Source

    Not directly comparable

  • ProgramBench

    DeepSeek V3—
    DeepSeek V4.1 Flash 101729.1%
    Source

    Not directly comparable

  • NL2Repo

    DeepSeek V3—
    DeepSeek V4.1 Flash 101768.2%
    Source

    Not directly comparable

  • OpenHarmony Bench

    DeepSeek V3—
    DeepSeek V4.1 Flash 101760.3%
    Source

    Not directly comparable

  • Bug Hunt Bench

    DeepSeek V3—
    DeepSeek V4.1 Flash 101739.1 fixes
    Source

    Not directly comparable

Reasoning

  • LongBench v2

    DeepSeek V348.7%
    Source
    DeepSeek V4.1 Flash 1017—

    Not directly comparable

  • STEMBench

    DeepSeek V3—
    DeepSeek V4.1 Flash 10172.49
    Source

    Not directly comparable

  • JevBench 1.4

    DeepSeek V3—
    DeepSeek V4.1 Flash 10174.77
    Source

    Not directly comparable

  • JevBench 1.5

    DeepSeek V3—
    DeepSeek V4.1 Flash 10176.65
    Source

    Not directly comparable

Multimodal

  • ExtractBench (LlamaIndex)

    DeepSeek V3—
    DeepSeek V4.1 Flash 101787.11%
    Source

    Not directly comparable

  • ParseBench

    DeepSeek V3—
    DeepSeek V4.1 Flash 101764.56%
    Source

    Not directly comparable

  • Chartography (tools)

    DeepSeek V3—
    DeepSeek V4.1 Flash 101783.0%
    Source

    Not directly comparable

  • BabyVision w/ Python

    DeepSeek V3—
    DeepSeek V4.1 Flash 101792.5%
    Source

    Not directly comparable

  • ZeroBench w/ Python

    DeepSeek V3—
    DeepSeek V4.1 Flash 101751.0%
    Source

    Not directly comparable

Knowledge

  • GPQA

    DeepSeek V359.1%
    Source
    DeepSeek V4.1 Flash 101790.9%
    Source

    DeepSeek V4.1 Flash 1017 leads this result

  • MMLU-Pro

    DeepSeek V375.9%
    Source
    DeepSeek V4.1 Flash 1017—

    Not directly comparable

  • SimpleQA

    DeepSeek V324.9%
    Source
    DeepSeek V4.1 Flash 1017—

    Not directly comparable

  • GPQA-D

    DeepSeek V3—
    DeepSeek V4.1 Flash 101790.9%
    Source

    Not directly comparable

  • HLE

    DeepSeek V3—
    DeepSeek V4.1 Flash 101742.6%
    Source

    Not directly comparable

Instruction following

  • IFEval

    DeepSeek V386.1%
    Source
    DeepSeek V4.1 Flash 1017—

    Not directly comparable

Math

  • AIME 2024

    DeepSeek V339.2%
    Source
    DeepSeek V4.1 Flash 1017—

    Not directly comparable

  • MATH-500

    DeepSeek V390.2%
    Source
    DeepSeek V4.1 Flash 1017—

    Not directly comparable

  • Apex

    DeepSeek V3—
    DeepSeek V4.1 Flash 101765.6%
    Source

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    DeepSeek V3—
    DeepSeek V4.1 Flash 101767.368%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    DeepSeek V3—
    DeepSeek V4.1 Flash 101726.829%
    Source

    Not directly comparable

41 public results · 1 shared

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Last updated October 25, 2026