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BenchLM
DeepSeek logo
Model A
DeepSeek V3.1

DeepSeek

52.67/100

Supported · Public rank #121

90% interval 37.1–68.2

DeepSeek V3.1 vs Gemini 3.5 Flash-Lite

Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Google logo
Model B
Gemini 3.5 Flash-Lite

Google

60.5/100

Supported · Public rank #72

90% interval 49.0–72.0

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.

  • Long documents

    Prompts that approach the documented context limit

    Gemini 3.5 Flash-Lite

    Gemini 3.5 Flash-Lite 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

    DeepSeek V3.1 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 V3.1 is not ranked on the public lane for agentic, so no winner is named for agentic.

    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

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. DeepSeek V3.1 does not fit this workload in one request. DeepSeek V3.1 has no comparable published API token rate.

    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

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 V3.1 only
0
Gemini 3.5 Flash-Lite only
10
Like-for-like categories
0 / 8

2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Coding

Directional only
DeepSeek V3.1
50.4
Estimated · #72/183
Gemini 3.5 Flash-Lite
43.6
Supported · #121/183
Basis
BenchAlign lane · 0 vs 4 public rows
Reading
Directional only

Knowledge

Directional only
DeepSeek V3.1
46.1
Estimated · #111/181
Gemini 3.5 Flash-Lite
53.0
Supported · #71/181
Basis
BenchAlign lane · 0 vs 2 public rows
Reading
Directional only

Agentic

Not comparable
DeepSeek V3.1
Not ranked
Gemini 3.5 Flash-Lite
43.4
Estimated · #104/151
Basis
BenchAlign lane · 0 vs 3 public rows
Reading
Not comparable

Reasoning

Not comparable
DeepSeek V3.1
54.6
Unranked · 2 rankable rows
Gemini 3.5 Flash-Lite
60.8
Unranked · 3 rankable rows
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Math

Not comparable
DeepSeek V3.1
Not ranked
Gemini 3.5 Flash-Lite
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
DeepSeek V3.1
Not ranked
Gemini 3.5 Flash-Lite
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
DeepSeek V3.1
Not ranked
Gemini 3.5 Flash-Lite
76.1
#16/48
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
DeepSeek V3.1
43.5
#96/120
Gemini 3.5 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 V3.1
Self-hosted; infrastructure cost varies
Fits in one request
Gemini 3.5 Flash-Lite
$0.00155
Fits in one request

DeepSeek V3.1 has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

DeepSeek V3.1
Self-hosted; infrastructure cost varies
Fits in one request
Gemini 3.5 Flash-Lite
$0.0225
Fits in one request

DeepSeek V3.1 has no comparable published API token rate.

Cache-heavy agent loop

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

DeepSeek V3.1
Self-hosted; infrastructure cost varies
Does not fit in one request
Cached-input rate unavailable
Gemini 3.5 Flash-Lite
$0.037
Fits in one request

DeepSeek V3.1 does not fit this workload in one request. DeepSeek V3.1 has no comparable published API token rate.

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 V3.1

No comparable hosted API rate

Gemini 3.5 Flash-Lite

$0.03 per 1M cached input tokens

Google Gemini API pricing

Reasoning profile

DeepSeek V3.1

Non-Reasoning

Gemini 3.5 Flash-Lite

Reasoning

Weight access

DeepSeek V3.1

Open Weight

Gemini 3.5 Flash-Lite

Proprietary

License

DeepSeek V3.1

Open Weight

Gemini 3.5 Flash-Lite

Proprietary

Release date

DeepSeek V3.1

2025-08-21

Gemini 3.5 Flash-Lite

2026-07-21

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
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Gemini 3.5 Flash-Lite has the larger documented window (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 evidence10 rows

Agentic

  • Terminal-Bench 2.0

    DeepSeek V3.1—
    Gemini 3.5 Flash-Lite54%
    Source

    Not directly comparable

  • OSWorld-Verified

    DeepSeek V3.1—
    Gemini 3.5 Flash-Lite74%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    DeepSeek V3.1—
    Gemini 3.5 Flash-Lite50.2%
    Source

    Not directly comparable

Coding

  • Terminal-Bench 2.0

    DeepSeek V3.1—
    Gemini 3.5 Flash-Lite54.0%
    Source

    Not directly comparable

  • SWE-bench Pro

    DeepSeek V3.1—
    Gemini 3.5 Flash-Lite54.2%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    DeepSeek V3.1—
    Gemini 3.5 Flash-Lite79.0%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    DeepSeek V3.1—
    Gemini 3.5 Flash-Lite75.0%
    Source

    Not directly comparable

Reasoning

  • MRCRv2

    DeepSeek V3.1—
    Gemini 3.5 Flash-Lite72.2%
    Source

    Not directly comparable

Knowledge

  • GPQA Diamond (Vals)

    DeepSeek V3.1—
    Gemini 3.5 Flash-Lite83.8%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    DeepSeek V3.1—
    Gemini 3.5 Flash-Lite85.8%
    Source

    Not directly comparable

Frequently asked questions

Which is better, DeepSeek V3.1 or Gemini 3.5 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 V3.1 or Gemini 3.5 Flash-Lite?

DeepSeek V3.1 scores higher for coding on the public lane, 50.4 to 43.6. DeepSeek V3.1 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 V3.1 or Gemini 3.5 Flash-Lite?

DeepSeek V3.1 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, DeepSeek V3.1 or Gemini 3.5 Flash-Lite?

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 V3.1 or Gemini 3.5 Flash-Lite?

Gemini 3.5 Flash-Lite has the larger documented context window: 1M, compared with 128K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 4, 2026

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