Pairwise benchmark snapshot · Aug 17, 2026noindex (thin pair)

Anthropic: Claude Sonnet 5 (batch) vs DeepSeek V3 benchmark

Name: Anthropic: Claude Sonnet 5 (batch) vs DeepSeek V3 AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Anthropic: Claude Sonnet 5 (batch) (Anthropic) and DeepSeek V3 (DeepSeek) using the latest snapshots we have as of Aug 17, 2026. DeepSeek V3 wins on output price at $1.03/1M. Anthropic: Claude Sonnet 5 (batch) has the larger context window (1M tokens). Every cell below is a dated snapshot from a named public source — we do not invent missing scores, and we do not run SWE-bench ourselves.

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Anthropic

Anthropic: Claude Sonnet 5 (batch)

Auto-discovered from OpenRouter (anthropic/claude-sonnet-5:batch). Preview until a second source matches.

$5/1M out

DeepSeek

DeepSeek V3

Prior DeepSeek flagship. Baseline for v3 vs v4.

Elo 1,310$1.0287/1M out

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Model A

Model B

Quick vs:vs Claude Opus 4.5 vs Claude Opus 4.6 vs Claude Sonnet 4.5 vs Claude 3.7 Sonnet

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Anthropic: Claude Sonnet 5 (batch) (1)vsDeepSeek V3 (8)

Anthropic: Claude Sonnet 5 (batch): 1W (11%)Overall: DeepSeek V3DeepSeek V3: 8W (89%)

← Anthropic: Claude Sonnet 5 (batch)DeepSeek V3 →

DeepSeek V3 (4/5)

Intelligence & Reasoning

Preference Elo, Coding proficiency, SWE-bench & LiveBench accuracy

Preference Elo

—vs1,310

Coding Elo

—vs—

SWE-bench

—vs48.6%

LiveBench

—vs55.4%

GPQA Diamond

—vs59.1%

Anthropic: Claude Sonnet 5 (batch): 0WDeepSeek V3: 4W

DeepSeek V3 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

—vs66 tok/s

Time to first token

—vs380 ms

Anthropic: Claude Sonnet 5 (batch): 0WDeepSeek V3: 2W

DeepSeek V3 (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$5/1Mvs$1.03/1M

Input price

$1/1Mvs$0.2574/1M

Context window

1Mvs164k

Anthropic: Claude Sonnet 5 (batch): 1WDeepSeek V3: 2W

Capability Matchup Radar

Multi-Dimensional Capability Radar

Each spoke is a skill. Farther from the center is better (0–100th percentile). Tap any dot to inspect details.

Tap any node to inspect

Percentile 0–100

Dimensional AdvantageDeepSeek V3 5/6

Preference Elo

No shared data

Throughput Speed

No shared data

Token Price Efficiency

DeepSeek V3

$3.97/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

—

EloHigh is better

1,310

LiveBench

High is better

—

LiveBenchHigh is better

55.4%

SWE-bench

High is better

—

SWE-benchHigh is better

48.6%

GPQA

High is better

—

GPQAHigh is better

59.1%

TTFT

Low is better

—

TTFTLow is better

380 ms

Speed

High is better

—

SpeedHigh is better

66 tok/s

In $

Low is better

$1.00 / 1M

In $Low is better

$0.26 / 1M+289%

Out $

Low is better

$5.00 / 1M

Out $Low is better

$1.03 / 1M+386%

Context

High is better

+510%1M

ContextHigh is better

164k

Interactive Simulator (USD)

Workload Cost & Savings Calculator

Simulate monthly production API costs in USD (US Dollar).

Save up to 78% with DeepSeek V3

Monthly Volume50M tokens/mo

Quick Presets:

Token Ratio (In vs Out)70% In / 30% Out

RAG / Search (10% out)Coding / Chat (50% out)

Anthropic: Claude Sonnet 5 (batch)$110.00 / mo

In: $35Out: $75

DeepSeek V3$24.44 / mo

In: $9.01Out: $15.43

Estimated Cost Delta

DeepSeek V3 is estimated to save $85.56/month ($1,027/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
DeepSeek V3:Pick DeepSeek V3 when you are optimizing output cost.
2
Anthropic: Claude Sonnet 5 (batch):Pick Anthropic: Claude Sonnet 5 (batch) for million-token RAG or long-document jobs.

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatDeepSeek V3

Lower output list price ($1.03/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGAnthropic: Claude Sonnet 5 (batch)

Larger window (1M).

Screenshots / visionDeepSeek V3

DeepSeek V3 is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	insufficient data	Need SWE-bench on both sides.
High-volume chat	DeepSeek V3	Lower output list price ($1.03/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Anthropic: Claude Sonnet 5 (batch)	Larger window (1M).
Screenshots / vision	DeepSeek V3	DeepSeek V3 is the side marked multimodal in the catalog.

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LIVE

Anthropic

Anthropic: Claude Sonnet 5 (batch)

Elo n/a

LiveBench—

SWE-bench—

Speed—

Output cost$5/1M

DeepSeek

DeepSeek V3

Elo 1,310

LiveBench55.4%

SWE-bench48.6%

Speed66 tok/s

Output cost$1.03/1M

Frequently Asked Questions

Which is better overall, Anthropic: Claude Sonnet 5 (batch) or DeepSeek V3?: We do not have preference Elo for both models, so we do not declare an overall winner. Compare the metrics that exist.
Which is better at coding, Anthropic: Claude Sonnet 5 (batch) or DeepSeek V3?: SWE-bench is missing for at least one model, so we do not rank coding from a single number.
Which is cheaper to run in production?: DeepSeek V3 output tokens are $1.03/1M versus $5/1M. Input prices and retry rates still move the real bill.
Which is faster, Anthropic: Claude Sonnet 5 (batch) or DeepSeek V3?: We do not have TTFT for both models.
Which has the larger context window?: Anthropic: Claude Sonnet 5 (batch) accepts 1M tokens versus 164k.
Can I self-host either model?: DeepSeek V3 is marked open-weights in our catalog. The other side is a closed API. Check the provider license before you ship weights.
Are these scores from CompareLLM’s own evals?: No. V1 aggregates public leaderboards (and optional first-party latency pings). Each cell names its source and date. Read /methodology.
What does preference Elo mean on this page?: Preference Elo is a crowd vote from LMArena / Arena. People see two hidden answers and pick the one they like more. The model that wins more often gets a higher Elo. That means people preferred it — not that it passed a school test. It is not SWE-bench, not accuracy, and not a number we invent. Anthropic: Claude Sonnet 5 (batch) and DeepSeek V3 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All Anthropic: Claude Sonnet 5 (batch) matchups →|All DeepSeek V3 matchups →

DeepSeek V3 vs DeepSeek Coder V2 (previous deepseek)DeepSeek V3 vs DeepSeek R1 (next deepseek)Claude Opus 4.5 vs DeepSeek V3 Claude Opus 4.6 vs DeepSeek V3 Claude Sonnet 4.5 vs DeepSeek V3 DeepSeek V4 Pro vs DeepSeek V3

Anthropic: Claude Sonnet 5 (batch) vs DeepSeek V3 benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Anthropic: Claude Sonnet 5 (batch) (1)vsDeepSeek V3 (8)

Anthropic: Claude Sonnet 5 (batch): 1W (11%)Overall: DeepSeek V3DeepSeek V3: 8W (89%)

← Anthropic: Claude Sonnet 5 (batch)DeepSeek V3 →

DeepSeek V3 (4/5)

Intelligence & Reasoning

Preference Elo, Coding proficiency, SWE-bench & LiveBench accuracy

Preference Elo

—vs1,310

Coding Elo

—vs—

SWE-bench

—vs48.6%

LiveBench

—vs55.4%

GPQA Diamond

—vs59.1%

Anthropic: Claude Sonnet 5 (batch): 0WDeepSeek V3: 4W

DeepSeek V3 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

—vs66 tok/s

Time to first token

—vs380 ms

Anthropic: Claude Sonnet 5 (batch): 0WDeepSeek V3: 2W

DeepSeek V3 (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$5/1Mvs$1.03/1M

Input price

$1/1Mvs$0.2574/1M

Context window

1Mvs164k

Anthropic: Claude Sonnet 5 (batch): 1WDeepSeek V3: 2W

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatDeepSeek V3

Lower output list price ($1.03/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGAnthropic: Claude Sonnet 5 (batch)

Larger window (1M).

Screenshots / visionDeepSeek V3

DeepSeek V3 is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	insufficient data	Need SWE-bench on both sides.
High-volume chat	DeepSeek V3	Lower output list price ($1.03/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Anthropic: Claude Sonnet 5 (batch)	Larger window (1M).
Screenshots / vision	DeepSeek V3	DeepSeek V3 is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, Anthropic: Claude Sonnet 5 (batch) or DeepSeek V3?

We do not have preference Elo for both models, so we do not declare an overall winner. Compare the metrics that exist.

Which is better at coding, Anthropic: Claude Sonnet 5 (batch) or DeepSeek V3?

SWE-bench is missing for at least one model, so we do not rank coding from a single number.

Which is cheaper to run in production?

DeepSeek V3 output tokens are $1.03/1M versus $5/1M. Input prices and retry rates still move the real bill.

Which is faster, Anthropic: Claude Sonnet 5 (batch) or DeepSeek V3?

We do not have TTFT for both models.

Which has the larger context window?

Anthropic: Claude Sonnet 5 (batch) accepts 1M tokens versus 164k.

Can I self-host either model?

DeepSeek V3 is marked open-weights in our catalog. The other side is a closed API. Check the provider license before you ship weights.

Are these scores from CompareLLM’s own evals?

No. V1 aggregates public leaderboards (and optional first-party latency pings). Each cell names its source and date. Read /methodology.

What does preference Elo mean on this page?

Preference Elo is a crowd vote from LMArena / Arena. People see two hidden answers and pick the one they like more. The model that wins more often gets a higher Elo. That means people preferred it — not that it passed a school test. It is not SWE-bench, not accuracy, and not a number we invent. Anthropic: Claude Sonnet 5 (batch) and DeepSeek V3 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.