DeepSeek V3 vs Gemini 3.7 Flash

Updated September 27, 2026. Rank says Gemini 3.7 Flash is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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 V3

DeepSeek

31.9/100

Supported · Public rank #145

90% interval 15.9–47.9

Model B

Gemini 3.7 Flash

Google

67.66/100

Supported · Public rank #19

90% interval 63.0–72.3

Shared results: 0
DeepSeek V3 only: 6
Gemini 3.7 Flash only: 25
Like-for-like categories: 0 / 8

Supported: DeepSeek V3 and Gemini 3.7 FlashHow 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
Gemini 3.7 Flash
Gemini 3.7 Flash has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
DeepSeek V3
DeepSeek V3 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
DeepSeek V3
DeepSeek V3 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 V3 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 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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. DeepSeek V3 does not fit this workload in one request.
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.

Pick the job you would use them for

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

18.9DeepSeek V360.3Gemini 3.7 Flash

Directional only · BenchAlign v5.7

Gemini 3.7 Flash scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboard Shortlist models for this job

Same basis rules as the category table below

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 v5.7 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

Directional only

DeepSeek V3: 11.1; Estimated · #102/105
Gemini 3.7 Flash: 58.6; Supported · #20/105
Basis: BenchAlign v5.7 lane · 0 vs 7 public rows
Reading: Directional only

Coding

Directional only

DeepSeek V3: 18.9; Estimated · #123/135
Gemini 3.7 Flash: 60.3; Supported · #17/135
Basis: BenchAlign v5.7 lane · 2 vs 6 public rows
Reading: Directional only

Knowledge

Directional only

DeepSeek V3: 30.0; Estimated · #130/158
Gemini 3.7 Flash: 71.1; Supported · #10/158
Basis: BenchAlign v5.7 lane · 2 vs 6 public rows
Reading: Directional only

Reasoning

Not comparable

DeepSeek V3: 42.2; Unranked · 2 rankable rows
Gemini 3.7 Flash: 77.8; Unranked · 5 rankable rows
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Multimodal

Not comparable

DeepSeek V3: Not ranked
Gemini 3.7 Flash: 82.6; #10/50
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Multilingual

Not comparable

DeepSeek V3: Not ranked
Gemini 3.7 Flash: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

DeepSeek V3: 38.2; #103/124
Gemini 3.7 Flash: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

DeepSeek V3: 26.0; Unranked · 1 rankable row
Gemini 3.7 Flash: Not ranked
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for DeepSeek V3 and Gemini 3.7 Flash
Category	DeepSeek V3	Gemini 3.7 Flash	Basis	Reading
Agentic	11.1Estimated · #102/105	58.6Supported · #20/105	Directional onlyBenchAlign v5.7 lane · 0 vs 7 public rows	Directional only
Coding	18.9Estimated · #123/135	60.3Supported · #17/135	Directional onlyBenchAlign v5.7 lane · 2 vs 6 public rows	Directional only
Knowledge	30.0Estimated · #130/158	71.1Supported · #10/158	Directional onlyBenchAlign v5.7 lane · 2 vs 6 public rows	Directional only
Reasoning	42.2Unranked · 2 rankable rows	77.8Unranked · 5 rankable rows	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Multimodal	Not ranked	82.6#10/50	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	38.2#103/124	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	26.0Unranked · 1 rankable row	Not ranked	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) 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 V3: $0.00082; Fits in one request
Gemini 3.7 Flash: $0.00262; Fits in one request

DeepSeek V3 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

DeepSeek V3: $0.0168; Fits in one request
Gemini 3.7 Flash: $0.04875; Fits in one request

DeepSeek V3 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 V3: $0.0304; Does not fit in one request
Gemini 3.7 Flash: $0.0675; Fits in one request

DeepSeek V3 does not fit this workload in one request.

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.

SpecificationDeepSeek V3Gemini 3.7 Flash

Context window

Maximum documented context; output-token limits may be lower.

DeepSeek V3

128K

Gemini 3.7 Flash

Google Gemini 3.7 Flash API documentation

API model ID

DeepSeek V3

Not sourced

Gemini 3.7 Flash

gemini-3.7-flash

Google Gemini 3.7 Flash API documentation

Cached-input rate

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

DeepSeek V3

$0.07 per 1M cached input tokens

Gemini 3.7 Flash

$0.075 per 1M cached input tokens

Google Gemini API pricing

Documented inputs

DeepSeek V3

Not sourced

Gemini 3.7 Flash

text, image, video, audio, pdf

Google Gemini 3.7 Flash API documentation

Documented outputs

DeepSeek V3

Not sourced

Gemini 3.7 Flash

text

Google Gemini 3.7 Flash API documentation

Provider availability

DeepSeek V3

Not sourced

Gemini 3.7 Flash

Generally Available · Gemini API, Google AI Studio, Gemini App - Spark, Gemini Enterprise App, Gemini Enterprise Agent Platform, Google Antigravity

Google DeepMind Gemini 3.7 Flash model card

Reasoning profile

DeepSeek V3

Non-Reasoning

Gemini 3.7 Flash

Reasoning

Weight access

DeepSeek V3

Open Weight

Gemini 3.7 Flash

Proprietary

License

DeepSeek V3

Open Weight

Gemini 3.7 Flash

Proprietary

Release date

DeepSeek V3

2024-12-26

Gemini 3.7 Flash

2026-08-13

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: $0.0168 vs $0.04875. Cache-heavy agent loop: $0.0304 vs $0.0675.
Context tradeoff: Gemini 3.7 Flash has the larger documented window (1M).

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

Questions

Which is better, DeepSeek V3 or Gemini 3.7 Flash?

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 or Gemini 3.7 Flash?

Gemini 3.7 Flash scores higher for coding on the public lane, 60.3 to 18.9. DeepSeek V3 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 or Gemini 3.7 Flash?

Gemini 3.7 Flash scores higher for agentic tasks on the public lane, 58.6 to 11.1. DeepSeek V3 is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, DeepSeek V3 or Gemini 3.7 Flash?

For the stated presets, chat costs $0.00082 on DeepSeek V3 and $0.00263 on Gemini 3.7 Flash; repository review costs $0.0168 and $0.04875; the cache-heavy agent loop costs $0.0304 and $0.0675. DeepSeek V3 does not fit this workload in one request.

Which has the larger context window, DeepSeek V3 or Gemini 3.7 Flash?

Gemini 3.7 Flash has the larger documented context window: 1M, compared with 128K.

Self-host vs API cost

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

DeepSeek V3

API / mo$1,028

Self-host / mo$18,221

Break-even1.2B/day

Gemini 3.7 Flash

API / mo$3,375

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 evidence31 rows

Agentic

Terminal-Bench 2.1
DeepSeek V3—
Gemini 3.7 Flash85.8%
Source
Not directly comparable
Terminal-Bench 3.0
DeepSeek V3—
Gemini 3.7 Flash14.9%
Source
Not directly comparable
AutomationBench
DeepSeek V3—
Gemini 3.7 Flash30.4%
Source
Not directly comparable
OSWorld 2.0
DeepSeek V3—
Gemini 3.7 Flash47.9%
Source
Not directly comparable
Agents' Last Exam
DeepSeek V3—
Gemini 3.7 Flash26.3%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
DeepSeek V3—
Gemini 3.7 Flash77.5%
Source
Not directly comparable
ApprenticeBench
DeepSeek V3—
Gemini 3.7 Flash16%
Source
Not directly comparable

Coding

LiveCodeBench
DeepSeek V337.6%
Source
Gemini 3.7 Flash—
Not directly comparable
SWE-bench Verified
DeepSeek V342%
Source
Gemini 3.7 Flash—
Not directly comparable
FrontierCode 1.1 Main
DeepSeek V3—
Gemini 3.7 Flash43.6%
Source
Not directly comparable
DeepSWE
DeepSeek V3—
Gemini 3.7 Flash65.3%
Source
Not directly comparable
Terminal-Bench 2.1
DeepSeek V3—
Gemini 3.7 Flash85.8%
Source
Not directly comparable
FrontierSWE v2
DeepSeek V3—
Gemini 3.7 Flash20.3%
Source
Not directly comparable
LiveCodeBench (Vals)
DeepSeek V3—
Gemini 3.7 Flash88.7%
Source
Not directly comparable
SWE-bench (Vals)
DeepSeek V3—
Gemini 3.7 Flash80.8%
Source
Not directly comparable

Reasoning

MRCR v2 64K-128K
DeepSeek V3—
Gemini 3.7 Flash97%
Source
Not directly comparable
ARC-AGI-1
DeepSeek V3—
Gemini 3.7 Flash95.50%
Source
Not directly comparable
ARC-AGI-2
DeepSeek V3—
Gemini 3.7 Flash84.6%
Source
Not directly comparable

Multimodal

CharXiv w/o tools
DeepSeek V3—
Gemini 3.7 Flash84.5%
Source
Not directly comparable
CharXiv
DeepSeek V3—
Gemini 3.7 Flash88.7%
Source
Not directly comparable
LVBench
DeepSeek V3—
Gemini 3.7 Flash85.4%
Source
Not directly comparable

Knowledge

GPQA
DeepSeek V359.1%
Source
Gemini 3.7 Flash—
Not directly comparable
MMLU-Pro
DeepSeek V375.9%
Source
Gemini 3.7 Flash—
Not directly comparable
HLE-Verified
DeepSeek V3—
Gemini 3.7 Flash53.6%
Source
Not directly comparable
LABBench2
DeepSeek V3—
Gemini 3.7 Flash82.1%
Source
Not directly comparable
BioMysteryBench (human-solvable)
DeepSeek V3—
Gemini 3.7 Flash87.1%
Source
Not directly comparable
BioMysteryBench (human-difficult)
DeepSeek V3—
Gemini 3.7 Flash43.5%
Source
Not directly comparable
GPQA Diamond (Vals)
DeepSeek V3—
Gemini 3.7 Flash93.9%
Source
Not directly comparable
MMLU-Pro (Vals)
DeepSeek V3—
Gemini 3.7 Flash90.1%
Source
Not directly comparable

Instruction following

IFEval
DeepSeek V386.1%
Source
Gemini 3.7 Flash—
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
DeepSeek V31.724%
Source
Gemini 3.7 Flash—
Not directly comparable

31 public results · 0 shared

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Last updated September 27, 2026

DeepSeek V3 vs Gemini 3.7 Flash

Decision reading

Which one for your work

Long documents

Chat turn cost

Repository review cost

Coding work

Agentic work

Cache-heavy agent loop cost

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Agentic

Coding

Knowledge

Reasoning

Multimodal

Multilingual

Instruction following

Math

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

If you already use one of these models

Questions

Which is better, DeepSeek V3 or Gemini 3.7 Flash?

Which is better for coding, DeepSeek V3 or Gemini 3.7 Flash?

Which is better for agentic tasks, DeepSeek V3 or Gemini 3.7 Flash?

Which costs less, DeepSeek V3 or Gemini 3.7 Flash?

Which has the larger context window, DeepSeek V3 or Gemini 3.7 Flash?

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Instruction following

Math

Related comparisons

Watch DeepSeek V3 vs Gemini 3.7 Flash