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DeepSeek V4.1 Flash 1017 vs ERNIE 5.5 Flash Preview

Updated November 11, 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

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 5 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

65.13/100

Supported · Public rank #32

90% interval 59.9–70.4

Model B
Baidu logo
ERNIE 5.5 Flash Preview

Baidu

—

Evidence status unavailable

90% interval unavailable

API price per 1M tokens

DeepSeek V4.1 Flash 1017: $0.30 input / $1.20 output

ERNIE 5.5 Flash Preview: No comparable API price

Context window

DeepSeek V4.1 Flash 1017: 1M

ERNIE 5.5 Flash Preview: 1M

Released

DeepSeek V4.1 Flash 1017: October 17, 2026

ERNIE 5.5 Flash Preview: November 11, 2026

Shared results
5
DeepSeek V4.1 Flash 1017 only
32
ERNIE 5.5 Flash Preview only
10
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.

No workload recommendation clears the current evidence threshold.

Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.

Show secondary and unsupported calls

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    ERNIE 5.5 Flash Preview 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

    ERNIE 5.5 Flash Preview 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

  • 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.

63.1DeepSeek V4.1 Flash 1017—ERNIE 5.5 Flash Preview

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

Or describe your own job

The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.

Describe the task and constraints. This selector recommends models; it cannot carry out the task or access your files and accounts.

We keep what you type here, drafts included, for 180 days, and after that only the job and requirements read from it. Nothing is tied to your IP address or account. Leave out names and other personal details. Privacy

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.

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, reasoning, multimodal, 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
61.8
Supported · #22/132
ERNIE 5.5 Flash Preview
Not ranked
Basis
BenchAlign v5.8 lane · 12 vs 10 public rows
Reading
Not comparable

Coding

Not comparable
DeepSeek V4.1 Flash 1017
63.1
Supported · #15/158
ERNIE 5.5 Flash Preview
Not ranked
Basis
BenchAlign v5.8 lane · 11 vs 3 public rows
Reading
Not comparable

Reasoning

Not comparable
DeepSeek V4.1 Flash 1017
58.7
Estimated · #35/158
ERNIE 5.5 Flash Preview
Not ranked
Basis
BenchAlign v5.8 lane · 3 vs 0 public rows
Reading
Not comparable

Multimodal

Not comparable
DeepSeek V4.1 Flash 1017
63.9
Supported · #28/67
ERNIE 5.5 Flash Preview
Not ranked
Basis
BenchAlign v5.8 lane · 5 vs 1 public rows
Reading
Not comparable

Knowledge

Not comparable
DeepSeek V4.1 Flash 1017
63.7
Supported · #35/179
ERNIE 5.5 Flash Preview
Not ranked
Basis
BenchAlign v5.8 lane · 3 vs 1 public rows
Reading
Not comparable

Multilingual

Not comparable
DeepSeek V4.1 Flash 1017
Not ranked
ERNIE 5.5 Flash Preview
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
DeepSeek V4.1 Flash 1017
Not ranked
ERNIE 5.5 Flash Preview
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
DeepSeek V4.1 Flash 1017
65.6
Unranked · 3 rankable rows
ERNIE 5.5 Flash Preview
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, reasoning, multimodal, 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
ERNIE 5.5 Flash Preview
API rate not published
Fits in one request

ERNIE 5.5 Flash Preview has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

DeepSeek V4.1 Flash 1017
$0.0186
Fits in one request
ERNIE 5.5 Flash Preview
API rate not published
Fits in one request

ERNIE 5.5 Flash Preview has no comparable published API token rate.

Cache-heavy agent loop

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

DeepSeek V4.1 Flash 1017
$0.0192
Fits in one request
ERNIE 5.5 Flash Preview
API rate not published
Fits in one request
Cached-input rate unavailable

ERNIE 5.5 Flash Preview 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.

Cached-input rate

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

DeepSeek V4.1 Flash 1017

$0.006 per 1M cached input tokens

DeepSeek: Models & Pricing

ERNIE 5.5 Flash Preview

No comparable hosted API rate

Baidu Qianfan model page

Reasoning profile

DeepSeek V4.1 Flash 1017

Reasoning

ERNIE 5.5 Flash Preview

Reasoning

Weight access

DeepSeek V4.1 Flash 1017

Open Weight

ERNIE 5.5 Flash Preview

Pending

License

DeepSeek V4.1 Flash 1017

Open Weight

ERNIE 5.5 Flash Preview

Pending

Release date

DeepSeek V4.1 Flash 1017

2026-10-17

ERNIE 5.5 Flash Preview

2026-11-11

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
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Both models list 1M.
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, DeepSeek V4.1 Flash 1017 or ERNIE 5.5 Flash Preview?

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, DeepSeek V4.1 Flash 1017 or ERNIE 5.5 Flash Preview?

ERNIE 5.5 Flash Preview 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 ERNIE 5.5 Flash Preview?

ERNIE 5.5 Flash Preview 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 ERNIE 5.5 Flash Preview?

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 V4.1 Flash 1017 or ERNIE 5.5 Flash Preview?

Both models list the same context window, 1M.

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-evenNot reached at 70% load
ERNIE 5.5 Flash Preview
API / moNo API price on file
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 evidence47 rows

Agentic

  • Terminal-Bench 2.1

    DeepSeek V4.1 Flash 101790.6%
    Source
    ERNIE 5.5 Flash Preview—

    Not directly comparable

  • terminalBench3

    DeepSeek V4.1 Flash 101730%
    Source
    ERNIE 5.5 Flash Preview—

    Not directly comparable

  • Terminal-Bench 4.0

    DeepSeek V4.1 Flash 101742.30%
    Source
    ERNIE 5.5 Flash Preview46.50%
    Source

    ERNIE 5.5 Flash Preview leads this result

  • CyberGym

    DeepSeek V4.1 Flash 101788.1%
    Source
    ERNIE 5.5 Flash Preview—

    Not directly comparable

  • ExploitGym

    DeepSeek V4.1 Flash 101715.3%
    Source
    ERNIE 5.5 Flash Preview—

    Not directly comparable

  • HLE w/ tools

    DeepSeek V4.1 Flash 101763.9%
    Source
    ERNIE 5.5 Flash Preview—

    Not directly comparable

  • AutomationBench

    DeepSeek V4.1 Flash 101760.5%
    Source
    ERNIE 5.5 Flash Preview61.9%
    Source

    ERNIE 5.5 Flash Preview leads this result

  • Agents' Last Exam

    DeepSeek V4.1 Flash 101735.4%
    Source
    ERNIE 5.5 Flash Preview—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    DeepSeek V4.1 Flash 101774.5%
    Source
    ERNIE 5.5 Flash Preview—

    Not directly comparable

  • CWE-bench v1

    DeepSeek V4.1 Flash 101755.0%
    Source
    ERNIE 5.5 Flash Preview—

    Not directly comparable

  • MazeBench

    DeepSeek V4.1 Flash 10171%
    Source
    ERNIE 5.5 Flash Preview—

    Not directly comparable

  • CWE-bench

    DeepSeek V4.1 Flash 101757.6%
    Source
    ERNIE 5.5 Flash Preview—

    Not directly comparable

  • Terminal-Bench-Science 0.1

    DeepSeek V4.1 Flash 1017—
    ERNIE 5.5 Flash Preview21.0%
    Source

    Not directly comparable

  • APEX-Agents

    DeepSeek V4.1 Flash 1017—
    ERNIE 5.5 Flash Preview64.2%
    Source

    Not directly comparable

  • DRACO

    DeepSeek V4.1 Flash 1017—
    ERNIE 5.5 Flash Preview84.9%
    Source

    Not directly comparable

  • SpreadsheetBench 2

    DeepSeek V4.1 Flash 1017—
    ERNIE 5.5 Flash Preview36.4%
    Source

    Not directly comparable

  • Agents' Last Exam (CLI)

    DeepSeek V4.1 Flash 1017—
    ERNIE 5.5 Flash Preview29.0%
    Source

    Not directly comparable

  • Toolathlon-Verified

    DeepSeek V4.1 Flash 1017—
    ERNIE 5.5 Flash Preview75.0%
    Source

    Not directly comparable

  • MCP Atlas

    DeepSeek V4.1 Flash 1017—
    ERNIE 5.5 Flash Preview84.6%
    Source

    Not directly comparable

  • BrowseComp

    DeepSeek V4.1 Flash 1017—
    ERNIE 5.5 Flash Preview87.4%
    Source

    Not directly comparable

Coding

  • ScarfBench

    DeepSeek V4.1 Flash 101769.0%
    Source
    ERNIE 5.5 Flash Preview—

    Not directly comparable

  • Codeforces

    DeepSeek V4.1 Flash 10173471.0
    Source
    ERNIE 5.5 Flash Preview—

    Not directly comparable

  • Terminal-Bench 2.1

    DeepSeek V4.1 Flash 101790.6%
    Source
    ERNIE 5.5 Flash Preview—

    Not directly comparable

  • terminalBench3

    DeepSeek V4.1 Flash 101730%
    Source
    ERNIE 5.5 Flash Preview—

    Not directly comparable

  • DeepSWE

    DeepSeek V4.1 Flash 101777.0%
    Source
    ERNIE 5.5 Flash Preview71.2%
    Source

    DeepSeek V4.1 Flash 1017 leads this result

  • ProgramBench

    DeepSeek V4.1 Flash 101729.1%
    Source
    ERNIE 5.5 Flash Preview29.5%
    Source

    ERNIE 5.5 Flash Preview leads this result

  • NL2Repo

    DeepSeek V4.1 Flash 101768.2%
    Source
    ERNIE 5.5 Flash Preview64.8%
    Source

    DeepSeek V4.1 Flash 1017 leads this result

  • OpenHarmony Bench

    DeepSeek V4.1 Flash 101760.3%
    Source
    ERNIE 5.5 Flash Preview—

    Not directly comparable

  • Bug Hunt Bench

    DeepSeek V4.1 Flash 101739.1 fixes
    Source
    ERNIE 5.5 Flash Preview—

    Not directly comparable

  • FrontierCode 2.0

    DeepSeek V4.1 Flash 101719.3%
    Source
    ERNIE 5.5 Flash Preview—

    Not directly comparable

  • CursorBench 4.0

    DeepSeek V4.1 Flash 101747.7%
    Source
    ERNIE 5.5 Flash Preview—

    Not directly comparable

Reasoning

  • STEMBench

    DeepSeek V4.1 Flash 10172.54
    Source
    ERNIE 5.5 Flash Preview—

    Not directly comparable

  • JevBench 1.4

    DeepSeek V4.1 Flash 10174.77
    Source
    ERNIE 5.5 Flash Preview—

    Not directly comparable

  • JevBench 1.5

    DeepSeek V4.1 Flash 10176.65
    Source
    ERNIE 5.5 Flash Preview—

    Not directly comparable

Multimodal

  • ExtractBench (LlamaIndex)

    DeepSeek V4.1 Flash 101787.11%
    Source
    ERNIE 5.5 Flash Preview—

    Not directly comparable

  • ParseBench

    DeepSeek V4.1 Flash 101764.38%
    Source
    ERNIE 5.5 Flash Preview—

    Not directly comparable

  • Chartography (tools)

    DeepSeek V4.1 Flash 101783.0%
    Source
    ERNIE 5.5 Flash Preview—

    Not directly comparable

  • BabyVision w/ Python

    DeepSeek V4.1 Flash 101792.5%
    Source
    ERNIE 5.5 Flash Preview—

    Not directly comparable

  • ZeroBench w/ Python

    DeepSeek V4.1 Flash 101751.0%
    Source
    ERNIE 5.5 Flash Preview—

    Not directly comparable

  • OfficeQA Pro v2

    DeepSeek V4.1 Flash 1017—
    ERNIE 5.5 Flash Preview67.9%
    Source

    Not directly comparable

Knowledge

  • GPQA

    DeepSeek V4.1 Flash 101790.9%
    Source
    ERNIE 5.5 Flash Preview—

    Not directly comparable

  • GPQA-D

    DeepSeek V4.1 Flash 101790.9%
    Source
    ERNIE 5.5 Flash Preview—

    Not directly comparable

  • HLE

    DeepSeek V4.1 Flash 101742.6%
    Source
    ERNIE 5.5 Flash Preview—

    Not directly comparable

  • HLE w/o tools

    DeepSeek V4.1 Flash 1017—
    ERNIE 5.5 Flash Preview46.4%
    Source

    Not directly comparable

Math

  • Apex

    DeepSeek V4.1 Flash 101765.6%
    Source
    ERNIE 5.5 Flash Preview—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    DeepSeek V4.1 Flash 101767.368%
    Source
    ERNIE 5.5 Flash Preview—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    DeepSeek V4.1 Flash 101726.829%
    Source
    ERNIE 5.5 Flash Preview—

    Not directly comparable

47 public results · 5 shared

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Last updated November 11, 2026