Pairwise benchmark snapshot · Aug 17, 2026

Llama 3.1 70B vs Qwen2.5 Coder 32B Instruct benchmark

Name: Llama 3.1 70B vs Qwen2.5 Coder 32B Instruct AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Llama 3.1 70B (Meta) and Qwen2.5 Coder 32B Instruct (Alibaba) using the latest snapshots we have as of Aug 17, 2026. On preference Elo, Qwen2.5 Coder 32B Instruct is ahead by 185 points (1,425 vs 1,240; lmarena (Aug 17, 2026)). On SWE-bench coding, Qwen2.5 Coder 32B Instruct resolves 65.2% versus 40.2% (swebench (Aug 17, 2026)). Llama 3.1 70B wins on output price at $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 (6)vsQwen2.5 Coder 32B Instruct (4)

Llama 3.1 70B: 6W (60%)Overall: Llama 3.1 70BQwen2.5 Coder 32B Instruct: 4W (40%)

← Llama 3.1 70BQwen2.5 Coder 32B Instruct →

Qwen2.5 Coder 32B Instruct (4/5)

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%vs—

Llama 3.1 70B: 1WQwen2.5 Coder 32B Instruct: 4W

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: 2WQwen2.5 Coder 32B Instruct: 0W

Llama 3.1 70B (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.4/1Mvs$1/1M

Input price

$0.4/1Mvs$0.66/1M

Context window

131kvs33k

Llama 3.1 70B: 3WQwen2.5 Coder 32B Instruct: 0W

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.

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Percentile 0–100

Dimensional AdvantageEven 3–3

Preference Elo

Qwen2.5 Coder 32B Instruct

185 pts advantage

Throughput Speed

No shared data

Token Price Efficiency

Llama 3.1 70B

$0.60/1M 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

—

TTFT

Low is better

170 ms

TTFTLow is better

—

Speed

High is better

125 tok/s

SpeedHigh is better

—

In $

Low is better

+65%$0.40 / 1M

In $Low is better

$0.66 / 1M

Out $

Low is better

+150%$0.40 / 1M

Out $Low is better

$1.00 / 1M

Context

High is better

+300%131k

ContextHigh is better

33k

Interactive Simulator (USD)

Workload Cost & Savings Calculator

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

Save up to 48% 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: $14Out: $6

Qwen2.5 Coder 32B Instruct$38.10 / mo

In: $23.1Out: $15

Estimated Cost Delta

Llama 3.1 70B is estimated to save $18.10/month ($217/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
Qwen2.5 Coder 32B Instruct:Pick Qwen2.5 Coder 32B Instruct 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
Qwen2.5 Coder 32B Instruct:Pick Qwen2.5 Coder 32B Instruct as the default general assistant.

Recommended Workload Routing

Repo / coding agentsQwen2.5 Coder 32B Instruct

Higher SWE-bench (65.2%).

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 RAGLlama 3.1 70B

Larger window (131k).

Screenshots / visionQwen2.5 Coder 32B Instruct

Qwen2.5 Coder 32B Instruct is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	Qwen2.5 Coder 32B Instruct	Higher SWE-bench (65.2%).
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	Llama 3.1 70B	Larger window (131k).
Screenshots / vision	Qwen2.5 Coder 32B Instruct	Qwen2.5 Coder 32B Instruct 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

Qwen2.5 Coder 32B Instruct

Elo 1,425

LiveBench64.8%

SWE-bench65.2%

Speed—

Output cost$1/1M

Frequently Asked Questions

Which is better overall, Llama 3.1 70B or Qwen2.5 Coder 32B Instruct?: Qwen2.5 Coder 32B Instruct 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 Qwen2.5 Coder 32B Instruct?: Qwen2.5 Coder 32B Instruct 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 $1/1M. Input prices and retry rates still move the real bill.
Which is faster, Llama 3.1 70B or Qwen2.5 Coder 32B Instruct?: We do not have TTFT for both models.
Which has the larger context window?: Llama 3.1 70B accepts 131k tokens versus 33k.
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 Qwen2.5 Coder 32B Instruct 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 Qwen2.5 Coder 32B Instruct matchups →

Llama 3.1 70B vs Llama 3.1 405B (previous llama)Llama 3.1 70B vs Llama 3.3 70B (next llama)Qwen2.5 Coder 32B Instruct vs Qwen 2.5 Plus (next qwen)Qwen 3 Max vs Qwen2.5 Coder 32B Instruct Llama 4 Maverick vs Qwen2.5 Coder 32B Instruct Qwen QwQ 32B vs Qwen2.5 Coder 32B Instruct

Llama 3.1 70B vs Qwen2.5 Coder 32B Instruct benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Llama 3.1 70B (6)vsQwen2.5 Coder 32B Instruct (4)

Llama 3.1 70B: 6W (60%)Overall: Llama 3.1 70BQwen2.5 Coder 32B Instruct: 4W (40%)

← Llama 3.1 70BQwen2.5 Coder 32B Instruct →

Qwen2.5 Coder 32B Instruct (4/5)

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%vs—

Llama 3.1 70B: 1WQwen2.5 Coder 32B Instruct: 4W

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: 2WQwen2.5 Coder 32B Instruct: 0W

Llama 3.1 70B (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.4/1Mvs$1/1M

Input price

$0.4/1Mvs$0.66/1M

Context window

131kvs33k

Llama 3.1 70B: 3WQwen2.5 Coder 32B Instruct: 0W

Recommended Workload Routing

Repo / coding agentsQwen2.5 Coder 32B Instruct

Higher SWE-bench (65.2%).

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 RAGLlama 3.1 70B

Larger window (131k).

Screenshots / visionQwen2.5 Coder 32B Instruct

Qwen2.5 Coder 32B Instruct is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	Qwen2.5 Coder 32B Instruct	Higher SWE-bench (65.2%).
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	Llama 3.1 70B	Larger window (131k).
Screenshots / vision	Qwen2.5 Coder 32B Instruct	Qwen2.5 Coder 32B Instruct is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, Llama 3.1 70B or Qwen2.5 Coder 32B Instruct?

Qwen2.5 Coder 32B Instruct 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 Qwen2.5 Coder 32B Instruct?

Qwen2.5 Coder 32B Instruct 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 $1/1M. Input prices and retry rates still move the real bill.

Which is faster, Llama 3.1 70B or Qwen2.5 Coder 32B Instruct?

We do not have TTFT for both models.

Which has the larger context window?

Llama 3.1 70B accepts 131k tokens versus 33k.

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 Qwen2.5 Coder 32B Instruct Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.