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

Llama 3.1 70B vs Qwen: Qwen3 235B A22B Thinking 2507 benchmark

Name: Llama 3.1 70B vs Qwen: Qwen3 235B A22B Thinking 2507 AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Llama 3.1 70B (Meta) and Qwen: Qwen3 235B A22B Thinking 2507 (Alibaba) using the latest snapshots we have as of Aug 17, 2026. Llama 3.1 70B wins on output price at $0.4/1M. Qwen: Qwen3 235B A22B Thinking 2507 has the larger context window (262k 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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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.

Llama 3.1 70B (7)vsQwen: Qwen3 235B A22B Thinking 2507 (2)

Llama 3.1 70B: 7W (78%)Overall: Llama 3.1 70BQwen: Qwen3 235B A22B Thinking 2507: 2W (22%)

← Llama 3.1 70BQwen: Qwen3 235B A22B Thinking 2507 →

Llama 3.1 70B (4/5)

Intelligence & Reasoning

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

Preference Elo

1,240vs—

Coding Elo

—vs—

SWE-bench

40.2%vs—

LiveBench

46.8%vs—

GPQA Diamond

48%vs—

Llama 3.1 70B: 4WQwen: Qwen3 235B A22B Thinking 2507: 0W

Llama 3.1 70B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

125 tok/svs—

Time to first token

170 msvs—

Llama 3.1 70B: 2WQwen: Qwen3 235B A22B Thinking 2507: 0W

Qwen: Qwen3 235B A22B Thinking 2507 (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.4/1Mvs$2.3/1M

Input price

$0.4/1Mvs$0.23/1M

Context window

131kvs262k

Llama 3.1 70B: 1WQwen: Qwen3 235B A22B Thinking 2507: 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 AdvantageLlama 3.1 70B 5/6

Preference Elo

No shared data

Throughput Speed

No shared data

Price Efficiency

Llama 3.1 70B

$1.90/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,240

EloHigh is better

—

LiveBench

High is better

46.8%

LiveBenchHigh is better

—

SWE-bench

High is better

40.2%

SWE-benchHigh is better

—

GPQA

High is better

48%

GPQAHigh is better

—

TTFT

Low is better

170 ms

TTFTLow is better

—

Speed

High is better

125 tok/s

SpeedHigh is better

—

In $

Low is better

$0.4/1M

In $Low is better

$0.23/1M+74%

Out $

Low is better

+475%$0.4/1M

Out $Low is better

$2.3/1M

Context

High is better

131k

ContextHigh is better

262k+100%

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 53% 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)

Llama 3.1 70B$20.00 / mo

In: $14.00Out: $6.00

Qwen: Qwen3 235B A22B Thinking 2507$42.55 / mo

In: $8.05Out: $34.50

Estimated Cost Delta

Llama 3.1 70B is estimated to save $22.55/month ($271/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
Llama 3.1 70B:Pick Llama 3.1 70B when you are optimizing output cost.

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatLlama 3.1 70B

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

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGQwen: Qwen3 235B A22B Thinking 2507

Larger window (262k).

Screenshots / visionQwen: Qwen3 235B A22B Thinking 2507

Qwen: Qwen3 235B A22B Thinking 2507 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	Llama 3.1 70B	Lower output list price ($0.4/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Qwen: Qwen3 235B A22B Thinking 2507	Larger window (262k).
Screenshots / vision	Qwen: Qwen3 235B A22B Thinking 2507	Qwen: Qwen3 235B A22B Thinking 2507 is the side marked multimodal in the catalog.

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LIVE

Llama 3.1 70B

Elo 1,240

LiveBench46.8%

SWE-bench40.2%

Speed125 tok/s

Output cost$0.4/1M

Alibaba

Qwen: Qwen3 235B A22B Thinking 2507

Elo n/a

LiveBench—

SWE-bench—

Speed—

Output cost$2.3/1M

Frequently Asked Questions

Which is better overall, Llama 3.1 70B or Qwen: Qwen3 235B A22B Thinking 2507?: 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, Llama 3.1 70B or Qwen: Qwen3 235B A22B Thinking 2507?: 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?: Llama 3.1 70B output tokens are $0.4/1M versus $2.3/1M. Input prices and retry rates still move the real bill.
Which is faster, Llama 3.1 70B or Qwen: Qwen3 235B A22B Thinking 2507?: We do not have TTFT for both models.
Which has the larger context window?: Qwen: Qwen3 235B A22B Thinking 2507 accepts 262k tokens versus 131k.
Can I self-host either model?: Llama 3.1 70B 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. Llama 3.1 70B and Qwen: Qwen3 235B A22B Thinking 2507 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All Llama 3.1 70B matchups →|All Qwen: Qwen3 235B A22B Thinking 2507 matchups →

Llama 3.1 70B vs Llama 3.1 405B (previous llama)Llama 3.1 70B vs Llama 3.3 70B (next llama)Qwen 3 Max vs Llama 3.1 70B Llama 4 Maverick vs Llama 3.1 70B Qwen QwQ 32B vs Llama 3.1 70B Qwen3.8 27B vs Llama 3.1 70B

Llama 3.1 70B vs Qwen: Qwen3 235B A22B Thinking 2507 benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Llama 3.1 70B (7)vsQwen: Qwen3 235B A22B Thinking 2507 (2)

Llama 3.1 70B: 7W (78%)Overall: Llama 3.1 70BQwen: Qwen3 235B A22B Thinking 2507: 2W (22%)

← Llama 3.1 70BQwen: Qwen3 235B A22B Thinking 2507 →

Llama 3.1 70B (4/5)

Intelligence & Reasoning

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

Preference Elo

1,240vs—

Coding Elo

—vs—

SWE-bench

40.2%vs—

LiveBench

46.8%vs—

GPQA Diamond

48%vs—

Llama 3.1 70B: 4WQwen: Qwen3 235B A22B Thinking 2507: 0W

Llama 3.1 70B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

125 tok/svs—

Time to first token

170 msvs—

Llama 3.1 70B: 2WQwen: Qwen3 235B A22B Thinking 2507: 0W

Qwen: Qwen3 235B A22B Thinking 2507 (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.4/1Mvs$2.3/1M

Input price

$0.4/1Mvs$0.23/1M

Context window

131kvs262k

Llama 3.1 70B: 1WQwen: Qwen3 235B A22B Thinking 2507: 2W

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatLlama 3.1 70B

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

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGQwen: Qwen3 235B A22B Thinking 2507

Larger window (262k).

Screenshots / visionQwen: Qwen3 235B A22B Thinking 2507

Qwen: Qwen3 235B A22B Thinking 2507 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	Llama 3.1 70B	Lower output list price ($0.4/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Qwen: Qwen3 235B A22B Thinking 2507	Larger window (262k).
Screenshots / vision	Qwen: Qwen3 235B A22B Thinking 2507	Qwen: Qwen3 235B A22B Thinking 2507 is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, Llama 3.1 70B or Qwen: Qwen3 235B A22B Thinking 2507?

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, Llama 3.1 70B or Qwen: Qwen3 235B A22B Thinking 2507?

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?

Llama 3.1 70B output tokens are $0.4/1M versus $2.3/1M. Input prices and retry rates still move the real bill.

Which is faster, Llama 3.1 70B or Qwen: Qwen3 235B A22B Thinking 2507?

We do not have TTFT for both models.

Which has the larger context window?

Qwen: Qwen3 235B A22B Thinking 2507 accepts 262k tokens versus 131k.

Can I self-host either model?

Llama 3.1 70B 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. Llama 3.1 70B and Qwen: Qwen3 235B A22B Thinking 2507 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.