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DeepSeek V4 Pro 0813 vs Gemini 4 Argon

Updated September 30, 2026. Rank says Gemini 4 Argon is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

Gemini 4 Argon has the higher public score estimate, 64.59 versus 64, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 4 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 Pro 0813

DeepSeek

64/100

Estimated · Public rank #36

90% interval 52.5–75.5

Model B
Google logo
Gemini 4 Argon

Google

64.59/100

Estimated · Public rank #32

90% interval 53.1–76.1

Shared results
4
DeepSeek V4 Pro 0813 only
38
Gemini 4 Argon only
13
Like-for-like categories
1 / 8
Estimated: DeepSeek V4 Pro 0813 and Gemini 4 ArgonHow 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.

  • Coding work

    Code generation, repair, and software-engineering tasks

    Gemini 4 Argon

    Gemini 4 Argon leads on the public coding lane, 68.4 to 49.5, with Supported evidence for both models, although the 90% intervals overlap.

    Confidence: limited

  • Chat turn cost

    1K fresh input + 500 output tokens

    DeepSeek V4 Pro 0813

    DeepSeek V4 Pro 0813 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 Pro 0813

    DeepSeek V4 Pro 0813 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 V4 Pro 0813

    DeepSeek V4 Pro 0813 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    Gemini 4 Argon is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    Not enough matched evidence

    A complete context comparison is not sourced.

    Confidence: limited

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.

49.5DeepSeek V4 Pro 081368.4Gemini 4 Argon

Like-for-like · BenchAlign v5.7

Gemini 4 Argon leads the like-for-like coding row, although the 90% intervals overlap.

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

What is actually comparable

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

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

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 v5.7 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.

Coding

Like-for-like
DeepSeek V4 Pro 0813
49.5
Supported · #46/144
Gemini 4 Argon
68.4
Supported · #8/144
Basis
BenchAlign v5.7 lane · 15 vs 4 public rows
Reading
Gemini 4 Argon leads · intervals overlap

Agentic

Directional only
DeepSeek V4 Pro 0813
53.1
Supported · #39/119
Gemini 4 Argon
63.7
Estimated · #14/119
Basis
BenchAlign v5.7 lane · 11 vs 7 public rows
Reading
Directional only

Knowledge

Directional only
DeepSeek V4 Pro 0813
64.0
Estimated · #33/170
Gemini 4 Argon
72.9
Supported · #11/170
Basis
BenchAlign v5.7 lane · 8 vs 1 public rows
Reading
Directional only

Reasoning

Not comparable
DeepSeek V4 Pro 0813
56.9
Unranked · 4 rankable rows
Gemini 4 Argon
77.1
Unranked · 3 rankable rows
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
DeepSeek V4 Pro 0813
Not ranked
Gemini 4 Argon
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
DeepSeek V4 Pro 0813
Not ranked
Gemini 4 Argon
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
DeepSeek V4 Pro 0813
Not ranked
Gemini 4 Argon
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
DeepSeek V4 Pro 0813
80.2
Unranked · 4 rankable rows
Gemini 4 Argon
Not ranked
Basis
Provisional lane · 1 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 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 V4 Pro 0813
$0.0033
Fits in one request
Gemini 4 Argon
$0.007
Fit state unavailable

DeepSeek V4 Pro 0813 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

DeepSeek V4 Pro 0813
$0.07788
Fits in one request
Gemini 4 Argon
$0.13
Fit state unavailable

DeepSeek V4 Pro 0813 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 Pro 0813
$0.0748
Fits in one request
Gemini 4 Argon
$0.16
Fit state unavailable

DeepSeek V4 Pro 0813 has the lower modeled cost

Costs use the listed standard API rates.

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.

Reasoning profile

DeepSeek V4 Pro 0813

Reasoning

Gemini 4 Argon

Reasoning

Weight access

DeepSeek V4 Pro 0813

Open Weight

Gemini 4 Argon

Proprietary

License

DeepSeek V4 Pro 0813

Open Weight

Gemini 4 Argon

Proprietary

Release date

DeepSeek V4 Pro 0813

2026-08-13

Gemini 4 Argon

2026-09-30

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
Gemini 4 Argon has the higher public score estimate, 64.59 versus 64, but the 90% score intervals overlap.
Workload cost
Repository review: $0.07788 vs $0.13. Cache-heavy agent loop: $0.0748 vs $0.16.
Context tradeoff
A complete documented context comparison is not available.
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, DeepSeek V4 Pro 0813 or Gemini 4 Argon?

Gemini 4 Argon has the higher public score estimate, 64.59 versus 64, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, DeepSeek V4 Pro 0813 or Gemini 4 Argon?

Gemini 4 Argon leads the public coding lane, 68.4 to 49.5, with Supported evidence for both models, although the 90% intervals overlap.

Which is better for agentic tasks, DeepSeek V4 Pro 0813 or Gemini 4 Argon?

Gemini 4 Argon scores higher for agentic tasks on the public lane, 63.7 to 53.1. Gemini 4 Argon 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 V4 Pro 0813 or Gemini 4 Argon?

For the stated presets, chat costs $0.0033 on DeepSeek V4 Pro 0813 and $0.007 on Gemini 4 Argon; repository review costs $0.07788 and $0.13; the cache-heavy agent loop costs $0.0748 and $0.16. Costs use the listed standard API rates.

Which has the larger context window, DeepSeek V4 Pro 0813 or Gemini 4 Argon?

A complete documented context-window comparison is not available.

Benchmark evidence

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

Browse raw public benchmark evidence55 rows

Agentic

  • Terminal-Bench 2.0

    DeepSeek V4 Pro 081367.9%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • Terminal-Bench 2.1

    DeepSeek V4 Pro 081387.9%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • BrowseComp

    DeepSeek V4 Pro 081383.4%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • HLE w/ tools

    DeepSeek V4 Pro 081360.0%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • MCP Atlas

    DeepSeek V4 Pro 081373.6%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • Toolathlon

    DeepSeek V4 Pro 081351.8%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • CyberGym

    DeepSeek V4 Pro 081383.3%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • Toolathlon-Verified

    DeepSeek V4 Pro 081374.1%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • Agents' Last Exam

    DeepSeek V4 Pro 081325.7%
    Source
    Gemini 4 Argon39.5%
    Source

    Gemini 4 Argon leads this result

  • AutomationBench

    DeepSeek V4 Pro 081331.8%
    Source
    Gemini 4 Argon51.3%
    Source

    Gemini 4 Argon leads this result

  • Terminal-Bench 2.1 (Vals)

    DeepSeek V4 Pro 081354.7%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • Finance Agent v2

    DeepSeek V4 Pro 0813—
    Gemini 4 Argon65.4%
    Source

    Not directly comparable

  • Terminal-Bench 4.0

    DeepSeek V4 Pro 0813—
    Gemini 4 Argon57.40%
    Source

    Not directly comparable

  • OSWorld 2.0

    DeepSeek V4 Pro 0813—
    Gemini 4 Argon69.2%
    Source

    Not directly comparable

  • CWE-bench v1

    DeepSeek V4 Pro 0813—
    Gemini 4 Argon68.0%
    Source

    Not directly comparable

  • Terminal-Bench-Science 0.1 (6x verifier timeout)

    DeepSeek V4 Pro 0813—
    Gemini 4 Argon57.6%
    Source

    Not directly comparable

Coding

  • LiveCodeBench Pass@1-COT

    DeepSeek V4 Pro 081393.5%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • Codeforces

    DeepSeek V4 Pro 08133206.0
    Source
    Gemini 4 Argon—

    Not directly comparable

  • SWE-bench Verified

    DeepSeek V4 Pro 081380.6%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • SWE-bench Pro

    DeepSeek V4 Pro 081355.4%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • SWE Multilingual

    DeepSeek V4 Pro 081376.2%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • Terminal-Bench 2.0

    DeepSeek V4 Pro 081367.9%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • Vibe Code Bench

    DeepSeek V4 Pro 081349.93%
    Source
    Gemini 4 Argon91.90%
    Source

    Gemini 4 Argon leads this result

  • Terminal-Bench 2.1

    DeepSeek V4 Pro 081387.9%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • NL2Repo

    DeepSeek V4 Pro 081361.5%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • DeepSWE

    DeepSeek V4 Pro 081362.7%
    Source
    Gemini 4 Argon77.9%
    Source

    Gemini 4 Argon leads this result

  • DSBench-FullStack

    DeepSeek V4 Pro 081371.1%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • DSBench-Hard

    DeepSeek V4 Pro 081367.2%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • OpenHarmony Bench

    DeepSeek V4 Pro 081359.0%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • LiveCodeBench (Vals)

    DeepSeek V4 Pro 081387.5%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • SWE-bench (Vals)

    DeepSeek V4 Pro 081396.4%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • FrontierSWE v2

    DeepSeek V4 Pro 0813—
    Gemini 4 Argon55.1%
    Source

    Not directly comparable

  • PostTrainBench v1.1

    DeepSeek V4 Pro 0813—
    Gemini 4 Argon45.3%
    Source

    Not directly comparable

Reasoning

  • MRCR 1M

    DeepSeek V4 Pro 081383.5%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • CorpusQA 1M

    DeepSeek V4 Pro 081362.0%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • ARC-AGI-1

    DeepSeek V4 Pro 081390.00%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • ARC-AGI-2

    DeepSeek V4 Pro 081361.3%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • Graphwalks BFS 128K

    DeepSeek V4 Pro 0813—
    Gemini 4 Argon99.7%
    Source

    Not directly comparable

  • GraphWalks BFS 256K–1M

    DeepSeek V4 Pro 0813—
    Gemini 4 Argon84.2%
    Source

    Not directly comparable

Multimodal

  • Chartography (no tools)

    DeepSeek V4 Pro 0813—
    Gemini 4 Argon71.6%
    Source

    Not directly comparable

  • LVBench

    DeepSeek V4 Pro 0813—
    Gemini 4 Argon91.7%
    Source

    Not directly comparable

Knowledge

  • MMLU-Pro

    DeepSeek V4 Pro 081387.5%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • SimpleQA

    DeepSeek V4 Pro 081357.9%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • Chinese-SimpleQA

    DeepSeek V4 Pro 081384.4%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • GPQA

    DeepSeek V4 Pro 081390.1%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • GPQA-D

    DeepSeek V4 Pro 081390.1%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • HLE

    DeepSeek V4 Pro 081342.7%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • GPQA Diamond (Vals)

    DeepSeek V4 Pro 081392.4%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • MMLU-Pro (Vals)

    DeepSeek V4 Pro 081387.0%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • LABBench2

    DeepSeek V4 Pro 0813—
    Gemini 4 Argon88.8%
    Source

    Not directly comparable

Instruction following

  • Gray Swan IPI (15 attempts)

    DeepSeek V4 Pro 0813—
    Gemini 4 Argon0.7%
    Source

    Not directly comparable

Math

  • HMMT Feb 2026

    DeepSeek V4 Pro 081395.2%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • IMOAnswerBench

    DeepSeek V4 Pro 081389.8%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • Apex

    DeepSeek V4 Pro 081338.3%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • Apex Shortlist

    DeepSeek V4 Pro 081390.2%
    Source
    Gemini 4 Argon—

    Not directly comparable

55 public results · 4 shared

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Last updated September 30, 2026