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Agents-A1 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 point estimate, 65.78 versus 54.43. Their conditional score ranges overlap. These ranges do not establish rank confidence. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
InternScience logo
Agents-A1

InternScience

54.43/100

Estimated · Public rank #72

Conditional range 40.1–68.8

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
2
Agents-A1 only
4
DeepSeek V4.1 Flash 1017 only
31
Like-for-like categories
0 / 8
Estimated: Agents-A1 · Supported: DeepSeek V4.1 Flash 1017. Conditional ranges do not establish rank confidence.How 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
Show secondary and unsupported calls

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    Agents-A1 is not ranked on the public lane for coding, so no winner is named for coding.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    Agents-A1 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    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

    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

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.

—Agents-A161.1DeepSeek V4.1 Flash 1017

Not comparable · BenchAlign v5.8

The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.

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.

1 category rests 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.

Agentic

Directional only
Agents-A1
47.9
Estimated · #51/132
DeepSeek V4.1 Flash 1017
61.7
Supported · #24/132
Basis
BenchAlign v5.8 lane · 3 vs 11 public rows
Reading
Directional only

Coding

Not comparable
Agents-A1
Not ranked
DeepSeek V4.1 Flash 1017
61.1
Supported · #18/153
Basis
BenchAlign v5.8 lane · 0 vs 8 public rows
Reading
Not comparable

Reasoning

Not comparable
Agents-A1
63.4
Unranked · 1 rankable row
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
Agents-A1
Not ranked
DeepSeek V4.1 Flash 1017
78.8
Unranked · 1 rankable row
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Agents-A1
Not ranked
DeepSeek V4.1 Flash 1017
64.4
Supported · #37/187
Basis
BenchAlign v5.8 lane · 1 vs 3 public rows
Reading
Not comparable

Multilingual

Not comparable
Agents-A1
Not ranked
DeepSeek V4.1 Flash 1017
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Agents-A1
Not ranked
DeepSeek V4.1 Flash 1017
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Agents-A1
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

Agents-A1
API rate not published
Fits in one request
DeepSeek V4.1 Flash 1017
$0.0009
Fits in one request

Agents-A1 has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Agents-A1
API rate not published
Fits in one request
DeepSeek V4.1 Flash 1017
$0.0186
Fits in one request

Agents-A1 has no comparable published API token rate.

Cache-heavy agent loop

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

Agents-A1
API rate not published
Fits in one request
Cached-input rate unavailable
DeepSeek V4.1 Flash 1017
$0.0192
Fits in one request

Agents-A1 has no comparable published API token 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.

Agents-A1

262K

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.

Agents-A1

No comparable hosted API rate

DeepSeek V4.1 Flash 1017

$0.006 per 1M cached input tokens

DeepSeek: Models & Pricing

Reasoning profile

Agents-A1

Reasoning

DeepSeek V4.1 Flash 1017

Reasoning

Weight access

Agents-A1

Open Weight

DeepSeek V4.1 Flash 1017

Open Weight

License

Agents-A1

Open Weight

DeepSeek V4.1 Flash 1017

Open Weight

Release date

Agents-A1

2026-06-26

DeepSeek V4.1 Flash 1017

2026-10-17

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 V4.1 Flash 1017 has the higher public point estimate, 65.78 versus 54.43. Their conditional score ranges overlap. These ranges do not establish rank confidence.
Workload cost
A complete comparable API-rate estimate is not available for both models.
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, Agents-A1 or DeepSeek V4.1 Flash 1017?

DeepSeek V4.1 Flash 1017 has the higher public point estimate, 65.78 versus 54.43. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Agents-A1 or DeepSeek V4.1 Flash 1017?

Agents-A1 is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Agents-A1 or DeepSeek V4.1 Flash 1017?

DeepSeek V4.1 Flash 1017 scores higher for agentic tasks on the public lane, 61.7 to 47.9. Agents-A1 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, Agents-A1 or DeepSeek V4.1 Flash 1017?

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, Agents-A1 or DeepSeek V4.1 Flash 1017?

DeepSeek V4.1 Flash 1017 has the larger documented context window: 1M, compared with 262K.

Self-host vs API cost

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

Agents-A1
API / mo$0
Self-host / mo$298
Break-even—
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 evidence37 rows

Agentic

  • BrowseComp

    Agents-A175.5%
    Source
    DeepSeek V4.1 Flash 1017—

    Not directly comparable

  • HLE w/ tools

    Agents-A147.6%
    Source
    DeepSeek V4.1 Flash 101763.9%
    Source

    DeepSeek V4.1 Flash 1017 leads this result

  • VITA-Bench

    Agents-A138.8%
    Source
    DeepSeek V4.1 Flash 1017—

    Not directly comparable

  • Terminal-Bench 2.1

    Agents-A1—
    DeepSeek V4.1 Flash 101790.6%
    Source

    Not directly comparable

  • terminalBench3

    Agents-A1—
    DeepSeek V4.1 Flash 101730%
    Source

    Not directly comparable

  • Terminal-Bench 4.0

    Agents-A1—
    DeepSeek V4.1 Flash 101742.30%
    Source

    Not directly comparable

  • CyberGym

    Agents-A1—
    DeepSeek V4.1 Flash 101788.1%
    Source

    Not directly comparable

  • ExploitGym

    Agents-A1—
    DeepSeek V4.1 Flash 101715.3%
    Source

    Not directly comparable

  • AutomationBench

    Agents-A1—
    DeepSeek V4.1 Flash 101760.5%
    Source

    Not directly comparable

  • Agents' Last Exam

    Agents-A1—
    DeepSeek V4.1 Flash 101735.4%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Agents-A1—
    DeepSeek V4.1 Flash 101774.5%
    Source

    Not directly comparable

  • CWE-bench v1

    Agents-A1—
    DeepSeek V4.1 Flash 101755.0%
    Source

    Not directly comparable

  • MazeBench

    Agents-A1—
    DeepSeek V4.1 Flash 10171%
    Source

    Not directly comparable

Coding

  • Codeforces

    Agents-A1—
    DeepSeek V4.1 Flash 10173471.0
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    Agents-A1—
    DeepSeek V4.1 Flash 101790.6%
    Source

    Not directly comparable

  • terminalBench3

    Agents-A1—
    DeepSeek V4.1 Flash 101730%
    Source

    Not directly comparable

  • DeepSWE

    Agents-A1—
    DeepSeek V4.1 Flash 101777.0%
    Source

    Not directly comparable

  • ProgramBench

    Agents-A1—
    DeepSeek V4.1 Flash 101729.1%
    Source

    Not directly comparable

  • NL2Repo

    Agents-A1—
    DeepSeek V4.1 Flash 101768.2%
    Source

    Not directly comparable

  • OpenHarmony Bench

    Agents-A1—
    DeepSeek V4.1 Flash 101760.3%
    Source

    Not directly comparable

  • Bug Hunt Bench

    Agents-A1—
    DeepSeek V4.1 Flash 101739.1 fixes
    Source

    Not directly comparable

Reasoning

  • LongBench v2

    Agents-A160.2%
    Source
    DeepSeek V4.1 Flash 1017—

    Not directly comparable

  • STEMBench

    Agents-A1—
    DeepSeek V4.1 Flash 10172.49
    Source

    Not directly comparable

  • JevBench 1.4

    Agents-A1—
    DeepSeek V4.1 Flash 10174.77
    Source

    Not directly comparable

  • JevBench 1.5

    Agents-A1—
    DeepSeek V4.1 Flash 10176.65
    Source

    Not directly comparable

Multimodal

  • ExtractBench (LlamaIndex)

    Agents-A1—
    DeepSeek V4.1 Flash 101787.11%
    Source

    Not directly comparable

  • ParseBench

    Agents-A1—
    DeepSeek V4.1 Flash 101764.56%
    Source

    Not directly comparable

  • Chartography (tools)

    Agents-A1—
    DeepSeek V4.1 Flash 101783.0%
    Source

    Not directly comparable

  • BabyVision w/ Python

    Agents-A1—
    DeepSeek V4.1 Flash 101792.5%
    Source

    Not directly comparable

  • ZeroBench w/ Python

    Agents-A1—
    DeepSeek V4.1 Flash 101751.0%
    Source

    Not directly comparable

Knowledge

  • HLE

    Agents-A147.6%
    Source
    DeepSeek V4.1 Flash 101742.6%
    Source

    Agents-A1 leads this result

  • GPQA

    Agents-A1—
    DeepSeek V4.1 Flash 101790.9%
    Source

    Not directly comparable

  • GPQA-D

    Agents-A1—
    DeepSeek V4.1 Flash 101790.9%
    Source

    Not directly comparable

Instruction following

  • IFEval

    Agents-A194.8%
    Source
    DeepSeek V4.1 Flash 1017—

    Not directly comparable

Math

  • Apex

    Agents-A1—
    DeepSeek V4.1 Flash 101765.6%
    Source

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    Agents-A1—
    DeepSeek V4.1 Flash 101767.368%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Agents-A1—
    DeepSeek V4.1 Flash 101726.829%
    Source

    Not directly comparable

37 public results · 2 shared

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Last updated October 25, 2026