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BenchLM

DeepSeek V4 Flash 0731 vs Gemma 4 31B

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.

3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

DeepSeek logo
Model A
DeepSeek V4 Flash 0731

DeepSeek

—

Evidence status unavailable

90% interval unavailable

Google logo
Model B
Gemma 4 31B

Google

52.4/100

Supported · Public rank #113

90% interval 29.1–75.7

Updated September 18, 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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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 Flash 0731

    DeepSeek V4 Flash 0731 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 V4 Flash 0731 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 Flash 0731 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

    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.

—DeepSeek V4 Flash 073142.3Gemma 4 31B

Not comparable · BenchAlign

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

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
3
DeepSeek V4 Flash 0731 only
37
Gemma 4 31B only
5
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 Flash 0731
Not ranked
Gemma 4 31B
45.4
Estimated · #86/154
Basis
BenchAlign lane · 11 vs 1 public rows
Reading
Not comparable

Coding

Not comparable
DeepSeek V4 Flash 0731
Not ranked
Gemma 4 31B
42.3
Supported · #108/154
Basis
BenchAlign lane · 15 vs 2 public rows
Reading
Not comparable

Reasoning

Not comparable
DeepSeek V4 Flash 0731
Not ranked
Gemma 4 31B
68.9
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
DeepSeek V4 Flash 0731
Not ranked
Gemma 4 31B
45.7
Supported · #108/184
Basis
BenchAlign lane · 8 vs 4 public rows
Reading
Not comparable

Math

Not comparable
DeepSeek V4 Flash 0731
79.9
Unranked · 4 rankable rows
Gemma 4 31B
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
DeepSeek V4 Flash 0731
Not ranked
Gemma 4 31B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
DeepSeek V4 Flash 0731
Not ranked
Gemma 4 31B
58.5
#30/48
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Instruction following

Not comparable
DeepSeek V4 Flash 0731
Not ranked
Gemma 4 31B
91.5
#12/124
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.

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.

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 Flash 0731
$0.00028
Fits in one request
Gemma 4 31B
Self-hosted; infrastructure cost varies
Fits in one request

Gemma 4 31B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

DeepSeek V4 Flash 0731
$0.00784
Fits in one request
Gemma 4 31B
Self-hosted; infrastructure cost varies
Fits in one request

Gemma 4 31B has no comparable published API token rate.

Cache-heavy agent loop

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

DeepSeek V4 Flash 0731
$0.00616
Fits in one request
Gemma 4 31B
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

Gemma 4 31B 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 V4 Flash 0731

$0.0028 per 1M cached input tokens

Gemma 4 31B

No comparable hosted API rate

Reasoning profile

DeepSeek V4 Flash 0731

Reasoning

Gemma 4 31B

Reasoning

Weight access

DeepSeek V4 Flash 0731

Proprietary

Gemma 4 31B

Open Weight

License

DeepSeek V4 Flash 0731

Proprietary

Gemma 4 31B

Open Weight

Release date

DeepSeek V4 Flash 0731

2026-07-31

Gemma 4 31B

2026-04-02

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
DeepSeek V4 Flash 0731 has the larger documented window (1M).

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

Self-host vs API cost

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

DeepSeek V4 Flash 0731
API / mo$0
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Gemma 4 31B
API / mo$0
Self-host / mo$429
Break-even—
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 evidence45 rows

Agentic

  • Terminal-Bench 2.0

    DeepSeek V4 Flash 073156.9%
    Source
    Gemma 4 31B—

    Not directly comparable

  • BrowseComp

    DeepSeek V4 Flash 073173.2%
    Source
    Gemma 4 31B—

    Not directly comparable

  • HLE w/ tools

    DeepSeek V4 Flash 073145.1%
    Source
    Gemma 4 31B—

    Not directly comparable

  • MCP Atlas

    DeepSeek V4 Flash 073169%
    Source
    Gemma 4 31B—

    Not directly comparable

  • Toolathlon

    DeepSeek V4 Flash 073147.8%
    Source
    Gemma 4 31B—

    Not directly comparable

  • Terminal-Bench 2.1

    DeepSeek V4 Flash 073182.7%
    Source
    Gemma 4 31B—

    Not directly comparable

  • CyberGym

    DeepSeek V4 Flash 073176.7%
    Source
    Gemma 4 31B—

    Not directly comparable

  • Toolathlon-Verified

    DeepSeek V4 Flash 073170.3%
    Source
    Gemma 4 31B—

    Not directly comparable

  • Agents' Last Exam

    DeepSeek V4 Flash 073125.2%
    Source
    Gemma 4 31B—

    Not directly comparable

  • AutomationBench

    DeepSeek V4 Flash 073125.1%
    Source
    Gemma 4 31B—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    DeepSeek V4 Flash 073167.0%
    Source
    Gemma 4 31B—

    Not directly comparable

  • Gert Labs

    DeepSeek V4 Flash 0731—
    Gemma 4 31B35.26%
    Source

    Not directly comparable

Coding

  • LiveCodeBench Pass@1-COT

    DeepSeek V4 Flash 073191.6%
    Source
    Gemma 4 31B—

    Not directly comparable

  • Codeforces

    DeepSeek V4 Flash 07313052.0
    Source
    Gemma 4 31B—

    Not directly comparable

  • SWE-bench Verified

    DeepSeek V4 Flash 073179%
    Source
    Gemma 4 31B—

    Not directly comparable

  • SWE-bench Pro

    DeepSeek V4 Flash 073152.6%
    Source
    Gemma 4 31B—

    Not directly comparable

  • SWE Multilingual

    DeepSeek V4 Flash 073173.3%
    Source
    Gemma 4 31B—

    Not directly comparable

  • Terminal-Bench 2.0

    DeepSeek V4 Flash 073156.9%
    Source
    Gemma 4 31B—

    Not directly comparable

  • Terminal-Bench 2.1

    DeepSeek V4 Flash 073182.7%
    Source
    Gemma 4 31B—

    Not directly comparable

  • NL2Repo

    DeepSeek V4 Flash 073154.2%
    Source
    Gemma 4 31B—

    Not directly comparable

  • DeepSWE

    DeepSeek V4 Flash 073154.4%
    Source
    Gemma 4 31B—

    Not directly comparable

  • DSBench-FullStack

    DeepSeek V4 Flash 073168.7%
    Source
    Gemma 4 31B—

    Not directly comparable

  • DSBench-Hard

    DeepSeek V4 Flash 073159.6%
    Source
    Gemma 4 31B—

    Not directly comparable

  • VulcanBench v3

    DeepSeek V4 Flash 073188.4%
    Source
    Gemma 4 31B—

    Not directly comparable

  • OpenHarmony Bench

    DeepSeek V4 Flash 073153.8%
    Source
    Gemma 4 31B—

    Not directly comparable

  • LiveCodeBench (Vals)

    DeepSeek V4 Flash 073187.3%
    Source
    Gemma 4 31B—

    Not directly comparable

  • SWE-bench (Vals)

    DeepSeek V4 Flash 073188.8%
    Source
    Gemma 4 31B—

    Not directly comparable

  • SWE-Rebench

    DeepSeek V4 Flash 0731—
    Gemma 4 31B41.6%
    Source

    Not directly comparable

  • React Native Evals

    DeepSeek V4 Flash 0731—
    Gemma 4 31B75.2%
    Source

    Not directly comparable

Reasoning

  • MRCR 1M

    DeepSeek V4 Flash 073178.7%
    Source
    Gemma 4 31B—

    Not directly comparable

  • CorpusQA 1M

    DeepSeek V4 Flash 073160.5%
    Source
    Gemma 4 31B—

    Not directly comparable

Knowledge

  • MMLU-Pro

    DeepSeek V4 Flash 073186.2%
    Source
    Gemma 4 31B85.2%
    Source

    DeepSeek V4 Flash 0731 leads this result

  • SimpleQA

    DeepSeek V4 Flash 073134.1%
    Source
    Gemma 4 31B—

    Not directly comparable

  • Chinese-SimpleQA

    DeepSeek V4 Flash 073178.9%
    Source
    Gemma 4 31B—

    Not directly comparable

  • GPQA

    DeepSeek V4 Flash 073188.1%
    Source
    Gemma 4 31B84.3%
    Source

    DeepSeek V4 Flash 0731 leads this result

  • GPQA-D

    DeepSeek V4 Flash 073188.1%
    Source
    Gemma 4 31B—

    Not directly comparable

  • HLE

    DeepSeek V4 Flash 073134.8%
    Source
    Gemma 4 31B26.5%
    Source

    DeepSeek V4 Flash 0731 leads this result

  • GPQA Diamond (Vals)

    DeepSeek V4 Flash 073189.9%
    Source
    Gemma 4 31B—

    Not directly comparable

  • MMLU-Pro (Vals)

    DeepSeek V4 Flash 073186.2%
    Source
    Gemma 4 31B—

    Not directly comparable

  • HLE w/o tools

    DeepSeek V4 Flash 0731—
    Gemma 4 31B19.5%
    Source

    Not directly comparable

Math

  • HMMT Feb 2026

    DeepSeek V4 Flash 073194.8%
    Source
    Gemma 4 31B—

    Not directly comparable

  • IMOAnswerBench

    DeepSeek V4 Flash 073188.4%
    Source
    Gemma 4 31B—

    Not directly comparable

  • Apex

    DeepSeek V4 Flash 073133.0%
    Source
    Gemma 4 31B—

    Not directly comparable

  • Apex Shortlist

    DeepSeek V4 Flash 073185.7%
    Source
    Gemma 4 31B—

    Not directly comparable

Multimodal

  • MMMU-Pro

    DeepSeek V4 Flash 0731—
    Gemma 4 31B76.9%
    Source

    Not directly comparable

Questions

Which is better, DeepSeek V4 Flash 0731 or Gemma 4 31B?

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 Flash 0731 or Gemma 4 31B?

DeepSeek V4 Flash 0731 is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, DeepSeek V4 Flash 0731 or Gemma 4 31B?

DeepSeek V4 Flash 0731 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, DeepSeek V4 Flash 0731 or Gemma 4 31B?

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 Flash 0731 or Gemma 4 31B?

DeepSeek V4 Flash 0731 has the larger documented context window: 1M, compared with 256K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 18, 2026

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