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DeepSeek V4.1 Flash 1017 vs Jev 1.14

Updated October 20, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

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Decision reading

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead. 0 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 V4.1 Flash 1017

DeepSeek

65.65/100

Supported · Public rank #35

90% interval 60.7–70.6

Model B
Jev 1.14

TypeSafe AI

—

Evidence status unavailable

90% interval unavailable

Shared results
0
DeepSeek V4.1 Flash 1017 only
32
Jev 1.14 only
0
Like-for-like categories
0 / 8
Supported: 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

    Jev 1.14

    Jev 1.14 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

    Jev 1.14

    Jev 1.14 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

    Jev 1.14 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

    Jev 1.14 is not ranked on the public lane for agentic, so no winner is named for agentic.

    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. Jev 1.14 does not fit this workload in one request. Jev 1.14 has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

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.

60.9DeepSeek V4.1 Flash 1017—Jev 1.14

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.

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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

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

Not comparable
DeepSeek V4.1 Flash 1017
61.7
Supported · #24/131
Jev 1.14
Not ranked
Basis
BenchAlign v5.8 lane · 10 vs 0 public rows
Reading
Not comparable

Coding

Not comparable
DeepSeek V4.1 Flash 1017
60.9
Supported · #18/152
Jev 1.14
Not ranked
Basis
BenchAlign v5.8 lane · 8 vs 0 public rows
Reading
Not comparable

Reasoning

Not comparable
DeepSeek V4.1 Flash 1017
82.5
Unranked · 4 rankable rows
Jev 1.14
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
DeepSeek V4.1 Flash 1017
77.9
Unranked · 1 rankable row
Jev 1.14
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
DeepSeek V4.1 Flash 1017
65.2
Supported · #36/187
Jev 1.14
Not ranked
Basis
BenchAlign v5.8 lane · 3 vs 0 public rows
Reading
Not comparable

Multilingual

Not comparable
DeepSeek V4.1 Flash 1017
Not ranked
Jev 1.14
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
DeepSeek V4.1 Flash 1017
Not ranked
Jev 1.14
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
DeepSeek V4.1 Flash 1017
70.1
Unranked · 3 rankable rows
Jev 1.14
Not ranked
Basis
Provisional lane · 2 vs 0 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 V4.1 Flash 1017
$0.0009
Fits in one request
Jev 1.14
$0.00004
Fits in one request

Jev 1.14 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

DeepSeek V4.1 Flash 1017
$0.0186
Fits in one request
Jev 1.14
$0.0021
Fits in one request

Jev 1.14 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 V4.1 Flash 1017
$0.0192
Fits in one request
Jev 1.14
$0.00924
Does not fit in one request
Cached input priced at the published list-input rate

Jev 1.14 does not fit this workload in one request. Jev 1.14 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.

Reasoning profile

DeepSeek V4.1 Flash 1017

Reasoning

Jev 1.14

Unspecified

Weight access

DeepSeek V4.1 Flash 1017

Open Weight

Jev 1.14

Proprietary

License

DeepSeek V4.1 Flash 1017

Open Weight

Jev 1.14

Proprietary

Release date

DeepSeek V4.1 Flash 1017

2026-10-17

Jev 1.14

2026-10-14

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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
Repository review: $0.0186 vs $0.0021. Cache-heavy agent loop: $0.0192 vs $0.00924.
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 V4.1 Flash 1017 or Jev 1.14?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, DeepSeek V4.1 Flash 1017 or Jev 1.14?

Jev 1.14 is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, DeepSeek V4.1 Flash 1017 or Jev 1.14?

Jev 1.14 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, DeepSeek V4.1 Flash 1017 or Jev 1.14?

For the stated presets, chat costs $0.0009 on DeepSeek V4.1 Flash 1017 and $0.00004 on Jev 1.14; repository review costs $0.0186 and $0.0021; the cache-heavy agent loop costs $0.0192 and $0.00924. Jev 1.14 does not fit this workload in one request. Jev 1.14 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, DeepSeek V4.1 Flash 1017 or Jev 1.14?

DeepSeek V4.1 Flash 1017 has the larger documented context window: 1M, compared with 64K.

Self-host vs API cost

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

DeepSeek V4.1 Flash 1017
API / mo$1,125
Self-host / mo$27,962
Break-even1.6B/day
Jev 1.14
API / mo$32
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 evidence32 rows

Agentic

  • Terminal-Bench 2.1

    DeepSeek V4.1 Flash 101790.6%
    Source
    Jev 1.14—

    Not directly comparable

  • terminalBench3

    DeepSeek V4.1 Flash 101730%
    Source
    Jev 1.14—

    Not directly comparable

  • Terminal-Bench 4.0

    DeepSeek V4.1 Flash 101742.30%
    Source
    Jev 1.14—

    Not directly comparable

  • CyberGym

    DeepSeek V4.1 Flash 101788.1%
    Source
    Jev 1.14—

    Not directly comparable

  • ExploitGym

    DeepSeek V4.1 Flash 101715.3%
    Source
    Jev 1.14—

    Not directly comparable

  • HLE w/ tools

    DeepSeek V4.1 Flash 101763.9%
    Source
    Jev 1.14—

    Not directly comparable

  • AutomationBench

    DeepSeek V4.1 Flash 101760.5%
    Source
    Jev 1.14—

    Not directly comparable

  • Agents' Last Exam

    DeepSeek V4.1 Flash 101735.4%
    Source
    Jev 1.14—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    DeepSeek V4.1 Flash 101774.5%
    Source
    Jev 1.14—

    Not directly comparable

  • CWE-bench v1

    DeepSeek V4.1 Flash 101755.0%
    Source
    Jev 1.14—

    Not directly comparable

Coding

  • Codeforces

    DeepSeek V4.1 Flash 10173471.0
    Source
    Jev 1.14—

    Not directly comparable

  • Terminal-Bench 2.1

    DeepSeek V4.1 Flash 101790.6%
    Source
    Jev 1.14—

    Not directly comparable

  • terminalBench3

    DeepSeek V4.1 Flash 101730%
    Source
    Jev 1.14—

    Not directly comparable

  • DeepSWE

    DeepSeek V4.1 Flash 101777.0%
    Source
    Jev 1.14—

    Not directly comparable

  • ProgramBench

    DeepSeek V4.1 Flash 101729.1%
    Source
    Jev 1.14—

    Not directly comparable

  • NL2Repo

    DeepSeek V4.1 Flash 101768.2%
    Source
    Jev 1.14—

    Not directly comparable

  • OpenHarmony Bench

    DeepSeek V4.1 Flash 101760.3%
    Source
    Jev 1.14—

    Not directly comparable

  • Bug Hunt Bench

    DeepSeek V4.1 Flash 101739.1 fixes
    Source
    Jev 1.14—

    Not directly comparable

Reasoning

  • STEMBench

    DeepSeek V4.1 Flash 10172.49
    Source
    Jev 1.14—

    Not directly comparable

  • JevBench 1.4

    DeepSeek V4.1 Flash 10174.77
    Source
    Jev 1.14—

    Not directly comparable

  • JevBench 1.5

    DeepSeek V4.1 Flash 10176.65
    Source
    Jev 1.14—

    Not directly comparable

Multimodal

  • ExtractBench (LlamaIndex)

    DeepSeek V4.1 Flash 101787.11%
    Source
    Jev 1.14—

    Not directly comparable

  • ParseBench

    DeepSeek V4.1 Flash 101765.72%
    Source
    Jev 1.14—

    Not directly comparable

  • Chartography (tools)

    DeepSeek V4.1 Flash 101783.0%
    Source
    Jev 1.14—

    Not directly comparable

  • BabyVision w/ Python

    DeepSeek V4.1 Flash 101792.5%
    Source
    Jev 1.14—

    Not directly comparable

  • ZeroBench w/ Python

    DeepSeek V4.1 Flash 101751.0%
    Source
    Jev 1.14—

    Not directly comparable

Knowledge

  • GPQA

    DeepSeek V4.1 Flash 101790.9%
    Source
    Jev 1.14—

    Not directly comparable

  • GPQA-D

    DeepSeek V4.1 Flash 101790.9%
    Source
    Jev 1.14—

    Not directly comparable

  • HLE

    DeepSeek V4.1 Flash 101742.6%
    Source
    Jev 1.14—

    Not directly comparable

Math

  • Apex

    DeepSeek V4.1 Flash 101765.6%
    Source
    Jev 1.14—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    DeepSeek V4.1 Flash 101767.368%
    Source
    Jev 1.14—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    DeepSeek V4.1 Flash 101726.829%
    Source
    Jev 1.14—

    Not directly comparable

32 public results · 0 shared

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Last updated October 20, 2026