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BenchLM
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Model A
DeepSeek V4.1 Flash

DeepSeek

—

Evidence status unavailable

90% interval unavailable

DeepSeek V4.1 Flash vs Gemini 3.1 Flash-Lite

Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

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Model B
Gemini 3.1 Flash-Lite

Google

56.23/100

Supported · Public rank #90

90% interval 43.7–68.7

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.

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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

    DeepSeek V4.1 Flash

    DeepSeek V4.1 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

    DeepSeek V4.1 Flash

    DeepSeek V4.1 Flash 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

    Gemini 3.1 Flash-Lite

    Gemini 3.1 Flash-Lite 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 V4.1 Flash 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

    DeepSeek V4.1 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

What is actually comparable

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

Shared results
0
DeepSeek V4.1 Flash only
21
Gemini 3.1 Flash-Lite only
8
Like-for-like categories
0 / 8

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign 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
Not ranked
Gemini 3.1 Flash-Lite
43.1
Estimated · #102/152
Basis
BenchAlign lane · 8 vs 2 public rows
Reading
Not comparable

Coding

Not comparable
DeepSeek V4.1 Flash
Not ranked
Gemini 3.1 Flash-Lite
38.0
Estimated · #124/151
Basis
BenchAlign lane · 6 vs 3 public rows
Reading
Not comparable

Reasoning

Not comparable
DeepSeek V4.1 Flash
Not ranked
Gemini 3.1 Flash-Lite
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
DeepSeek V4.1 Flash
Not ranked
Gemini 3.1 Flash-Lite
51.7
Estimated · #71/182
Basis
BenchAlign lane · 3 vs 2 public rows
Reading
Not comparable

Math

Not comparable
DeepSeek V4.1 Flash
Not ranked
Gemini 3.1 Flash-Lite
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
DeepSeek V4.1 Flash
Not ranked
Gemini 3.1 Flash-Lite
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
DeepSeek V4.1 Flash
Not ranked
Gemini 3.1 Flash-Lite
37.5
Unranked · 1 rankable row
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Instruction following

Not comparable
DeepSeek V4.1 Flash
Not ranked
Gemini 3.1 Flash-Lite
Not ranked
Basis
Provisional lane · 0 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) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

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.

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
$0.0009
Fits in one request
Gemini 3.1 Flash-Lite
$0.001
Fits in one request

DeepSeek V4.1 Flash has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

DeepSeek V4.1 Flash
$0.0186
Fits in one request
Gemini 3.1 Flash-Lite
$0.017
Fits in one request

Gemini 3.1 Flash-Lite 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
$0.0192
Fits in one request
Gemini 3.1 Flash-Lite
$0.025
Fits in one request

DeepSeek V4.1 Flash has the lower modeled cost

Costs use the listed standard API rates.

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

$0.006 per 1M cached input tokens

DeepSeek: Models & Pricing

Gemini 3.1 Flash-Lite

$0.025 per 1M cached input tokens

Google Gemini API pricing

Provider availability

DeepSeek V4.1 Flash

Generally Available · DeepSeek API, open weights

DeepSeek-V4.1-Flash release

Gemini 3.1 Flash-Lite

Not sourced

Reasoning profile

DeepSeek V4.1 Flash

Reasoning

Gemini 3.1 Flash-Lite

Non-Reasoning

Weight access

DeepSeek V4.1 Flash

Open Weight

Gemini 3.1 Flash-Lite

Proprietary

License

DeepSeek V4.1 Flash

Open Weight

Gemini 3.1 Flash-Lite

Proprietary

Release date

DeepSeek V4.1 Flash

2026-09-10

Gemini 3.1 Flash-Lite

2026-03-03

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 $0.017. Cache-heavy agent loop: $0.0192 vs $0.025.
Context tradeoff
Both models list 1M.

Run the same representative tasks against both endpoints before changing production traffic.

Benchmark evidence

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

Browse raw public benchmark evidence29 rows

Agentic

  • Terminal-Bench 2.1

    DeepSeek V4.1 Flash90.6%
    Source
    Gemini 3.1 Flash-Lite—

    Not directly comparable

  • terminalBench3

    DeepSeek V4.1 Flash30%
    Source
    Gemini 3.1 Flash-Lite—

    Not directly comparable

  • Terminal-Bench 4.0

    DeepSeek V4.1 Flash31.20%
    Source
    Gemini 3.1 Flash-Lite—

    Not directly comparable

  • CyberGym

    DeepSeek V4.1 Flash88.1%
    Source
    Gemini 3.1 Flash-Lite—

    Not directly comparable

  • ExploitGym

    DeepSeek V4.1 Flash15.3%
    Source
    Gemini 3.1 Flash-Lite—

    Not directly comparable

  • HLE w/ tools

    DeepSeek V4.1 Flash63.9%
    Source
    Gemini 3.1 Flash-Lite—

    Not directly comparable

  • AutomationBench

    DeepSeek V4.1 Flash54.8%
    Source
    Gemini 3.1 Flash-Lite—

    Not directly comparable

  • Agents' Last Exam

    DeepSeek V4.1 Flash31.8%
    Source
    Gemini 3.1 Flash-Lite—

    Not directly comparable

  • Gert Labs

    DeepSeek V4.1 Flash—
    Gemini 3.1 Flash-Lite38.46%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    DeepSeek V4.1 Flash—
    Gemini 3.1 Flash-Lite34.1%
    Source

    Not directly comparable

Coding

  • Codeforces

    DeepSeek V4.1 Flash3471.0
    Source
    Gemini 3.1 Flash-Lite—

    Not directly comparable

  • Terminal-Bench 2.1

    DeepSeek V4.1 Flash90.6%
    Source
    Gemini 3.1 Flash-Lite—

    Not directly comparable

  • terminalBench3

    DeepSeek V4.1 Flash30%
    Source
    Gemini 3.1 Flash-Lite—

    Not directly comparable

  • DeepSWE

    DeepSeek V4.1 Flash74.2%
    Source
    Gemini 3.1 Flash-Lite—

    Not directly comparable

  • ProgramBench

    DeepSeek V4.1 Flash20.3%
    Source
    Gemini 3.1 Flash-Lite—

    Not directly comparable

  • NL2Repo

    DeepSeek V4.1 Flash65.4%
    Source
    Gemini 3.1 Flash-Lite—

    Not directly comparable

  • Vibe Code Bench

    DeepSeek V4.1 Flash—
    Gemini 3.1 Flash-Lite0.00%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    DeepSeek V4.1 Flash—
    Gemini 3.1 Flash-Lite80.1%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    DeepSeek V4.1 Flash—
    Gemini 3.1 Flash-Lite62.8%
    Source

    Not directly comparable

Knowledge

  • GPQA

    DeepSeek V4.1 Flash90.9%
    Source
    Gemini 3.1 Flash-Lite—

    Not directly comparable

  • GPQA-D

    DeepSeek V4.1 Flash90.9%
    Source
    Gemini 3.1 Flash-Lite—

    Not directly comparable

  • HLE

    DeepSeek V4.1 Flash36.8%
    Source
    Gemini 3.1 Flash-Lite—

    Not directly comparable

  • GPQA Diamond (Vals)

    DeepSeek V4.1 Flash—
    Gemini 3.1 Flash-Lite81.1%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    DeepSeek V4.1 Flash—
    Gemini 3.1 Flash-Lite86.2%
    Source

    Not directly comparable

Math

  • Apex

    DeepSeek V4.1 Flash65.6%
    Source
    Gemini 3.1 Flash-Lite—

    Not directly comparable

Multimodal

  • Chartography (tools)

    DeepSeek V4.1 Flash78.9%
    Source
    Gemini 3.1 Flash-Lite—

    Not directly comparable

  • BabyVision w/ Python

    DeepSeek V4.1 Flash89.6%
    Source
    Gemini 3.1 Flash-Lite—

    Not directly comparable

  • ZeroBench w/ Python

    DeepSeek V4.1 Flash49.0%
    Source
    Gemini 3.1 Flash-Lite—

    Not directly comparable

  • CharXiv

    DeepSeek V4.1 Flash—
    Gemini 3.1 Flash-Lite73.2%
    Source

    Not directly comparable

Frequently asked questions

Which is better, DeepSeek V4.1 Flash or Gemini 3.1 Flash-Lite?

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 or Gemini 3.1 Flash-Lite?

DeepSeek V4.1 Flash 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 or Gemini 3.1 Flash-Lite?

DeepSeek V4.1 Flash 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 or Gemini 3.1 Flash-Lite?

For the stated presets, chat costs $0.0009 on DeepSeek V4.1 Flash and $0.001 on Gemini 3.1 Flash-Lite; repository review costs $0.0186 and $0.017; the cache-heavy agent loop costs $0.0192 and $0.025. Costs use the listed standard API rates.

Which has the larger context window, DeepSeek V4.1 Flash or Gemini 3.1 Flash-Lite?

Both models list the same context window, 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 10, 2026

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