Pairwise benchmark snapshot · Aug 17, 2026

Llama 3.1 70B vs Qwen 2.5 Coder 32B benchmark

Name: Llama 3.1 70B vs Qwen 2.5 Coder 32B AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Llama 3.1 70B (Meta) and Qwen 2.5 Coder 32B (Alibaba) using the latest snapshots we have as of Aug 17, 2026. On preference Elo, Qwen 2.5 Coder 32B is ahead by 185 points (1,425 vs 1,240; seed-bootstrap (Aug 1, 2026)). On SWE-bench coding, Qwen 2.5 Coder 32B resolves 65.2% versus 40.2% (seed-bootstrap (Aug 1, 2026)). Llama 3.1 70B streams faster (125 tok/s) while Llama 3.1 70B is the cheaper output token ($0.4/1M). Llama 3.1 70B has the larger context window (131k 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 (3)vsQwen 2.5 Coder 32B (6)

Llama 3.1 70B: 3W (33%)Overall: Qwen 2.5 Coder 32BQwen 2.5 Coder 32B: 6W (67%)

← Llama 3.1 70B1 tiesQwen 2.5 Coder 32B →

Qwen 2.5 Coder 32B (5/5)

Intelligence & Reasoning

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

Preference Elo

1,240vs1,425

Coding Elo

—vs1,495

SWE-bench

40.2%vs65.2%

LiveBench

46.8%vs64.8%

GPQA Diamond

48%vs74%

Llama 3.1 70B: 0WQwen 2.5 Coder 32B: 5W

Llama 3.1 70B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

125 tok/svs110 tok/s

Time to first token

170 msvs180 ms

Llama 3.1 70B: 2WQwen 2.5 Coder 32B: 0W

Tied (1/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.4/1Mvs$0.72/1M

Input price

$0.4/1Mvs$0.18/1M

Context window

131kvs131k

Llama 3.1 70B: 1WQwen 2.5 Coder 32B: 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 AdvantageQwen 2.5 Coder 32B 3/6

Preference Elo

Qwen 2.5 Coder 32B

185 pts advantage

Throughput Speed

Llama 3.1 70B

15 tok/s faster

Token Price Efficiency

Llama 3.1 70B

$0.32/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,240

EloHigh is better

1,425+15%

Code Elo

High is better

—

Code EloHigh is better

1,495

LiveBench

High is better

46.8%

LiveBenchHigh is better

64.8%+38%

SWE-bench

High is better

40.2%

SWE-benchHigh is better

65.2%+62%

GPQA

High is better

48%

GPQAHigh is better

74%+54%

TTFT

Low is better

+6%170 ms

TTFTLow is better

180 ms

Speed

High is better

+14%125 tok/s

SpeedHigh is better

110 tok/s

In $

Low is better

$0.40 / 1M

In $Low is better

$0.18 / 1M+122%

Out $

Low is better

+80%$0.40 / 1M

Out $Low is better

$0.72 / 1M

Context

High is better

131k

ContextHigh is better

131k

Interactive Simulator (USD)

Workload Cost & Savings Calculator

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

Save up to 14% with Qwen 2.5 Coder 32B

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: $14Out: $6

Qwen 2.5 Coder 32B$17.10 / mo

In: $6.3Out: $10.8

Estimated Cost Delta

Qwen 2.5 Coder 32B is estimated to save $2.90/month ($35/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
Qwen 2.5 Coder 32B:Pick Qwen 2.5 Coder 32B 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 agentsQwen 2.5 Coder 32B

Higher SWE-bench (65.2%).

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 RAGLlama 3.1 70B

Larger window (131k).

Screenshots / visionQwen 2.5 Coder 32B

Qwen 2.5 Coder 32B is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	Qwen 2.5 Coder 32B	Higher SWE-bench (65.2%).
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	Llama 3.1 70B	Larger window (131k).
Screenshots / vision	Qwen 2.5 Coder 32B	Qwen 2.5 Coder 32B is the side marked multimodal in the catalog.

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CompareLLM Matrix

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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 2.5 Coder 32B

Elo 1,425

LiveBench64.8%

SWE-bench65.2%

Speed110 tok/s

Output cost$0.72/1M

Frequently Asked Questions

Which is better overall, Llama 3.1 70B or Qwen 2.5 Coder 32B?: Qwen 2.5 Coder 32B has the higher preference Elo in our latest snapshot (1,425). “Better” still depends on coding, price, and latency — see the table.
Which is better at coding, Llama 3.1 70B or Qwen 2.5 Coder 32B?: Qwen 2.5 Coder 32B leads SWE-bench at 65.2% 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 $0.72/1M. Input prices and retry rates still move the real bill.
Which is faster, Llama 3.1 70B or Qwen 2.5 Coder 32B?: Llama 3.1 70B has the lower time-to-first-token (170 ms vs 180 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: Llama 3.1 70B accepts 131k tokens versus 131k.
Can I self-host either model?: Both Llama 3.1 70B and Qwen 2.5 Coder 32B 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. Llama 3.1 70B and Qwen 2.5 Coder 32B 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 2.5 Coder 32B matchups →

Llama 3.1 70B vs Llama 3.1 405B (previous llama)Llama 3.1 70B vs Llama 3.3 70B (next llama)Qwen 2.5 Coder 32B vs Qwen 2.5 Plus (previous qwen)Qwen 2.5 Coder 32B vs Qwen3 235B (next qwen)Llama 4 Maverick vs Qwen 2.5 Coder 32B Qwen QwQ 32B vs Qwen 2.5 Coder 32B

Llama 3.1 70B vs Qwen 2.5 Coder 32B benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Llama 3.1 70B (3)vsQwen 2.5 Coder 32B (6)

Llama 3.1 70B: 3W (33%)Overall: Qwen 2.5 Coder 32BQwen 2.5 Coder 32B: 6W (67%)

← Llama 3.1 70B1 tiesQwen 2.5 Coder 32B →

Qwen 2.5 Coder 32B (5/5)

Intelligence & Reasoning

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

Preference Elo

1,240vs1,425

Coding Elo

—vs1,495

SWE-bench

40.2%vs65.2%

LiveBench

46.8%vs64.8%

GPQA Diamond

48%vs74%

Llama 3.1 70B: 0WQwen 2.5 Coder 32B: 5W

Llama 3.1 70B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

125 tok/svs110 tok/s

Time to first token

170 msvs180 ms

Llama 3.1 70B: 2WQwen 2.5 Coder 32B: 0W

Tied (1/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.4/1Mvs$0.72/1M

Input price

$0.4/1Mvs$0.18/1M

Context window

131kvs131k

Llama 3.1 70B: 1WQwen 2.5 Coder 32B: 1W

Recommended Workload Routing

Repo / coding agentsQwen 2.5 Coder 32B

Higher SWE-bench (65.2%).

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 RAGLlama 3.1 70B

Larger window (131k).

Screenshots / visionQwen 2.5 Coder 32B

Qwen 2.5 Coder 32B is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	Qwen 2.5 Coder 32B	Higher SWE-bench (65.2%).
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	Llama 3.1 70B	Larger window (131k).
Screenshots / vision	Qwen 2.5 Coder 32B	Qwen 2.5 Coder 32B is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, Llama 3.1 70B or Qwen 2.5 Coder 32B?

Qwen 2.5 Coder 32B has the higher preference Elo in our latest snapshot (1,425). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, Llama 3.1 70B or Qwen 2.5 Coder 32B?

Qwen 2.5 Coder 32B leads SWE-bench at 65.2% 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 $0.72/1M. Input prices and retry rates still move the real bill.

Which is faster, Llama 3.1 70B or Qwen 2.5 Coder 32B?

Llama 3.1 70B has the lower time-to-first-token (170 ms vs 180 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

Llama 3.1 70B accepts 131k tokens versus 131k.

Can I self-host either model?

Both Llama 3.1 70B and Qwen 2.5 Coder 32B 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. Llama 3.1 70B and Qwen 2.5 Coder 32B Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.