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Model comparison

DeepSeek V3 vs Grok 4.3

Updated July 30, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

DeepSeek V3

DeepSeek

44.1/100

Supported · Public rank #154

90% interval 25.5–62.8

Grok 4.3

xAI

64.2/100

Supported · Public rank #36

90% interval 54.5–74.0

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 based on different benchmark sets are marked directional and do not name a winner.

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

    Grok 4.3

    Grok 4.3 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

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Agentic work

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

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    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

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 only
6
Grok 4.3 only
2
Like-for-like categories
0 / 8

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Agentic

Not comparable
DeepSeek V3
Not measured
Grok 4.3
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Coding

Not comparable
DeepSeek V3
38.9
Grok 4.3
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
DeepSeek V3
Not measured
Grok 4.3
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
DeepSeek V3
72.7
Grok 4.3
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Math

Not comparable
DeepSeek V3
1.7
Grok 4.3
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
DeepSeek V3
Not measured
Grok 4.3
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
DeepSeek V3
Not measured
Grok 4.3
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
DeepSeek V3
86.1
Grok 4.3
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

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
$0.00082
Fits in one request
Grok 4.3
$0.0025
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
Grok 4.3
$0.07
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
Grok 4.3
$0.09
Fits in one request

DeepSeek V3 does not fit this workload in one request.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

Context window

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

DeepSeek V3

128K

Grok 4.3

1M

API model ID

DeepSeek V3

Not sourced

Grok 4.3

Not sourced

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

Grok 4.3

$0.2 per 1M cached input tokens

Documented inputs

DeepSeek V3

Not sourced

Grok 4.3

Not sourced

Documented outputs

DeepSeek V3

Not sourced

Grok 4.3

Not sourced

Provider availability

DeepSeek V3

Not sourced

Grok 4.3

Not sourced

Reasoning profile

DeepSeek V3

Non-Reasoning

Grok 4.3

Reasoning

Weight access

DeepSeek V3

Open Weight

Grok 4.3

Proprietary

License

DeepSeek V3

Open Weight

Grok 4.3

Proprietary

Release date

DeepSeek V3

2024-12-26

Grok 4.3

2026-04-30

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
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 $0.07. Cache-heavy agent loop: $0.0304 vs $0.09.
Context tradeoff
Grok 4.3 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 V3
API / mo$1,028
Self-host / mo$18,221
Break-even1.2B/day
Grok 4.3
API / mo$2,813
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 evidence8 rows

Agentic

  • Gert Labs

    DeepSeek V3
    Grok 4.343.86%
    Source

    Not directly comparable

  • ResearchClawBench

    DeepSeek V3
    Grok 4.312.4%
    Source

    Not directly comparable

Coding

  • LiveCodeBench

    DeepSeek V337.6%
    Source
    Grok 4.3

    Not directly comparable

  • SWE-bench Verified

    DeepSeek V342%
    Source
    Grok 4.3

    Not directly comparable

Knowledge

  • GPQA

    DeepSeek V359.1%
    Source
    Grok 4.3

    Not directly comparable

  • MMLU-Pro

    DeepSeek V375.9%
    Source
    Grok 4.3

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    DeepSeek V31.724%
    Source
    Grok 4.3

    Not directly comparable

Instruction following

  • IFEval

    DeepSeek V386.1%
    Source
    Grok 4.3

    Not directly comparable

Frequently asked questions

Which is better, DeepSeek V3 or Grok 4.3?

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 Grok 4.3?

The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.

Which is better for agentic tasks, DeepSeek V3 or Grok 4.3?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, DeepSeek V3 or Grok 4.3?

For the stated presets, chat costs $0.00082 on DeepSeek V3 and $0.0025 on Grok 4.3; repository review costs $0.0168 and $0.07; the cache-heavy agent loop costs $0.0304 and $0.09. DeepSeek V3 does not fit this workload in one request.

Which has the larger context window, DeepSeek V3 or Grok 4.3?

Grok 4.3 has the larger documented context window: 1M, compared with 128K.

Related comparisons

Last updated July 30, 2026

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