Pairwise benchmark snapshot · Aug 17, 2026

DeepSeek V3 vs Llama 3.1 70B benchmark

Name: DeepSeek V3 vs Llama 3.1 70B AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares DeepSeek V3 (DeepSeek) and Llama 3.1 70B (Meta) using the latest snapshots we have as of Aug 17, 2026. On preference Elo, DeepSeek V3 is ahead by 70 points (1,310 vs 1,240; lmarena (Aug 17, 2026)). On SWE-bench coding, DeepSeek V3 resolves 48.6% versus 40.2% (swebench (Aug 17, 2026)). Llama 3.1 70B streams faster (125 tok/s) while Llama 3.1 70B is the cheaper output token ($0.4/1M). DeepSeek V3 has the larger context window (164k 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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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.

DeepSeek V3 (6)vsLlama 3.1 70B (3)

DeepSeek V3: 6W (67%)Overall: DeepSeek V3Llama 3.1 70B: 3W (33%)

← DeepSeek V3Llama 3.1 70B →

DeepSeek V3 (4/5)

Intelligence & Reasoning

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

Preference Elo

1,310vs1,240

Coding Elo

—vs—

SWE-bench

48.6%vs40.2%

LiveBench

55.4%vs46.8%

GPQA Diamond

59.1%vs48%

DeepSeek V3: 4WLlama 3.1 70B: 0W

Llama 3.1 70B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

66 tok/svs125 tok/s

Time to first token

380 msvs170 ms

DeepSeek V3: 0WLlama 3.1 70B: 2W

DeepSeek V3 (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$1.03/1Mvs$0.4/1M

Input price

$0.26/1Mvs$0.4/1M

Context window

164kvs131k

DeepSeek V3: 2WLlama 3.1 70B: 1W

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 4/6

Preference Elo

DeepSeek V3

70 pts advantage

Throughput Speed

Llama 3.1 70B

59 tok/s faster

Price Efficiency

Llama 3.1 70B

$0.63/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

+6%1,310

EloHigh is better

1,240

LiveBench

High is better

+18%55.4%

LiveBenchHigh is better

46.8%

SWE-bench

High is better

+21%48.6%

SWE-benchHigh is better

40.2%

GPQA

High is better

+23%59.1%

GPQAHigh is better

48%

TTFT

Low is better

380 ms

TTFTLow is better

170 ms+124%

Speed

High is better

66 tok/s

SpeedHigh is better

125 tok/s+89%

In $

Low is better

+55%$0.26/1M

In $Low is better

$0.4/1M

Out $

Low is better

$1.03/1M

Out $Low is better

$0.4/1M+157%

Context

High is better

+25%164k

ContextHigh is better

131k

Interactive Simulator

Workload Cost & Savings Calculator

Simulate monthly production API costs based on published $/1M tokens.

Save up to 18% with Llama 3.1 70B

Monthly Volume50M tokens/mo

Quick Presets:

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

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

DeepSeek V3$24.44 / mo

In: $9.01Out: $15.43

Llama 3.1 70B$20.00 / mo

In: $14.00Out: $6.00

Estimated Cost Delta

Llama 3.1 70B is estimated to save $4.44/month ($53/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
DeepSeek V3:Pick DeepSeek V3 when repo-level coding accuracy is the constraint.
2
Llama 3.1 70B:Pick Llama 3.1 70B when you are optimizing output cost.
3
Llama 3.1 70B:Pick Llama 3.1 70B when time-to-first-token matters more than peak Elo.

Recommended Workload Routing

Repo / coding agentsDeepSeek V3

Higher SWE-bench (48.6%).

High-volume chatLlama 3.1 70B

Lower output list price ($0.4/1M).

Voice / low-latency UILlama 3.1 70B

Lower TTFT (170 ms).

Long-document RAGDeepSeek V3

Larger window (164k).

Screenshots / visionDeepSeek V3

DeepSeek V3 is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	DeepSeek V3	Higher SWE-bench (48.6%).
High-volume chat	Llama 3.1 70B	Lower output list price ($0.4/1M).
Voice / low-latency UI	Llama 3.1 70B	Lower TTFT (170 ms).
Long-document RAG	DeepSeek V3	Larger window (164k).
Screenshots / vision	DeepSeek V3	DeepSeek V3 is the side marked multimodal in the catalog.

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LIVE

DeepSeek

DeepSeek V3

Elo 1,310

LiveBench55.4%

SWE-bench48.6%

Speed66 tok/s

Output cost$1.03/1M

Llama 3.1 70B

Elo 1,240

LiveBench46.8%

SWE-bench40.2%

Speed125 tok/s

Output cost$0.4/1M

Frequently Asked Questions

Which is better overall, DeepSeek V3 or Llama 3.1 70B?: DeepSeek V3 has the higher preference Elo in our latest snapshot (1,310). “Better” still depends on coding, price, and latency — see the table.
Which is better at coding, DeepSeek V3 or Llama 3.1 70B?: DeepSeek V3 leads SWE-bench at 48.6% vs 40.2%. SWE-bench is one harness, not your repo.
Which is cheaper to run in production?: Llama 3.1 70B output tokens are $0.4/1M versus $1.03/1M. Input prices and retry rates still move the real bill.
Which is faster, DeepSeek V3 or Llama 3.1 70B?: Llama 3.1 70B has the lower time-to-first-token (170 ms vs 380 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: DeepSeek V3 accepts 164k tokens versus 131k.
Can I self-host either model?: Both DeepSeek V3 and Llama 3.1 70B are marked open-weights. Hosting cost is not in this table.
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. DeepSeek V3 and Llama 3.1 70B Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All DeepSeek V3 matchups →|All Llama 3.1 70B matchups →

DeepSeek V3 vs DeepSeek Coder V2 (previous deepseek)DeepSeek V3 vs DeepSeek R1 (next deepseek)Llama 3.1 70B vs Llama 3.1 405B (previous llama)Llama 3.1 70B vs Llama 3.3 70B (next llama)Llama 4 Maverick vs DeepSeek V3 DeepSeek V4 Pro vs DeepSeek V3

DeepSeek V3 vs Llama 3.1 70B benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

DeepSeek V3 (6)vsLlama 3.1 70B (3)

DeepSeek V3: 6W (67%)Overall: DeepSeek V3Llama 3.1 70B: 3W (33%)

← DeepSeek V3Llama 3.1 70B →

DeepSeek V3 (4/5)

Intelligence & Reasoning

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

Preference Elo

1,310vs1,240

Coding Elo

—vs—

SWE-bench

48.6%vs40.2%

LiveBench

55.4%vs46.8%

GPQA Diamond

59.1%vs48%

DeepSeek V3: 4WLlama 3.1 70B: 0W

Llama 3.1 70B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

66 tok/svs125 tok/s

Time to first token

380 msvs170 ms

DeepSeek V3: 0WLlama 3.1 70B: 2W

DeepSeek V3 (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$1.03/1Mvs$0.4/1M

Input price

$0.26/1Mvs$0.4/1M

Context window

164kvs131k

DeepSeek V3: 2WLlama 3.1 70B: 1W

Recommended Workload Routing

Repo / coding agentsDeepSeek V3

Higher SWE-bench (48.6%).

High-volume chatLlama 3.1 70B

Lower output list price ($0.4/1M).

Voice / low-latency UILlama 3.1 70B

Lower TTFT (170 ms).

Long-document RAGDeepSeek V3

Larger window (164k).

Screenshots / visionDeepSeek V3

DeepSeek V3 is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	DeepSeek V3	Higher SWE-bench (48.6%).
High-volume chat	Llama 3.1 70B	Lower output list price ($0.4/1M).
Voice / low-latency UI	Llama 3.1 70B	Lower TTFT (170 ms).
Long-document RAG	DeepSeek V3	Larger window (164k).
Screenshots / vision	DeepSeek V3	DeepSeek V3 is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, DeepSeek V3 or Llama 3.1 70B?

DeepSeek V3 has the higher preference Elo in our latest snapshot (1,310). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, DeepSeek V3 or Llama 3.1 70B?

DeepSeek V3 leads SWE-bench at 48.6% vs 40.2%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

Llama 3.1 70B output tokens are $0.4/1M versus $1.03/1M. Input prices and retry rates still move the real bill.

Which is faster, DeepSeek V3 or Llama 3.1 70B?

Llama 3.1 70B has the lower time-to-first-token (170 ms vs 380 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

DeepSeek V3 accepts 164k tokens versus 131k.

Can I self-host either model?

Both DeepSeek V3 and Llama 3.1 70B are marked open-weights. Hosting cost is not in this table.

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. DeepSeek V3 and Llama 3.1 70B Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.