Pairwise benchmark snapshot · Aug 16, 2026

Llama 3.3 70B vs Qwen3.8 27B benchmark

Name: Llama 3.3 70B vs Qwen3.8 27B AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Llama 3.3 70B (Meta) and Qwen3.8 27B (Alibaba) using the latest snapshots we have as of Aug 16, 2026. On preference Elo, Qwen3.8 27B is ahead by 113 points (1,398 vs 1,285; seed-bootstrap (Aug 16, 2026)). On SWE-bench coding, Qwen3.8 27B resolves 58.8% versus 45.1% (seed-bootstrap (Aug 16, 2026)). Qwen3.8 27B streams faster (152 tok/s) while Llama 3.3 70B is the cheaper output token ($0.32/1M). Qwen3.8 27B 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

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Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Llama 3.3 70B (2)vsQwen3.8 27B (8)

Llama 3.3 70B: 2W (20%)Overall: Qwen3.8 27BQwen3.8 27B: 8W (80%)

← Llama 3.3 70BQwen3.8 27B →

Qwen3.8 27B (5/5)

Intelligence & Reasoning

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

Preference Elo

1,285vs1,398

Coding Elo

—vs1,422

SWE-bench

45.1%vs58.8%

LiveBench

49.8%vs60.6%

GPQA Diamond

65.7%vs73.4%

Llama 3.3 70B: 0WQwen3.8 27B: 5W

Qwen3.8 27B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

140 tok/svs152 tok/s

Time to first token

160 msvs140 ms

Llama 3.3 70B: 0WQwen3.8 27B: 2W

Llama 3.3 70B (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.32/1Mvs$3.2/1M

Input price

$0.1/1Mvs$0.45/1M

Context window

131kvs262k

Llama 3.3 70B: 2WQwen3.8 27B: 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 AdvantageQwen3.8 27B 5/6

Preference Elo

Qwen3.8 27B

113 pts advantage

Throughput Speed

Qwen3.8 27B

12 tok/s faster

Price Efficiency

Llama 3.3 70B

$2.88/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,285

EloHigh is better

1,398+9%

Code Elo

High is better

—

Code EloHigh is better

1,422

LiveBench

High is better

49.8%

LiveBenchHigh is better

60.6%+22%

SWE-bench

High is better

45.1%

SWE-benchHigh is better

58.8%+30%

GPQA

High is better

65.7%

GPQAHigh is better

73.4%+12%

TTFT

Low is better

160 ms

TTFTLow is better

140 ms+14%

Speed

High is better

140 tok/s

SpeedHigh is better

152 tok/s+9%

In $

Low is better

+350%$0.1/1M

In $Low is better

$0.45/1M

Out $

Low is better

+900%$0.32/1M

Out $Low is better

$3.2/1M

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 87% with Llama 3.3 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 3.3 70B$8.30 / mo

In: $3.50Out: $4.80

Qwen3.8 27B$63.75 / mo

In: $15.75Out: $48.00

Estimated Cost Delta

Llama 3.3 70B is estimated to save $55.45/month ($665/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
Qwen3.8 27B:Pick Qwen3.8 27B when repo-level coding accuracy is the constraint.
2
Llama 3.3 70B:Pick Llama 3.3 70B when you are optimizing output cost.
3
Qwen3.8 27B:Pick Qwen3.8 27B when time-to-first-token matters more than peak Elo.

Recommended Workload Routing

Repo / coding agentsQwen3.8 27B

Higher SWE-bench (58.8%).

High-volume chatLlama 3.3 70B

Lower output list price ($0.32/1M).

Voice / low-latency UIQwen3.8 27B

Lower TTFT (140 ms).

Long-document RAGQwen3.8 27B

Larger window (262k).

Screenshots / visionQwen3.8 27B

Qwen3.8 27B is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	Qwen3.8 27B	Higher SWE-bench (58.8%).
High-volume chat	Llama 3.3 70B	Lower output list price ($0.32/1M).
Voice / low-latency UI	Qwen3.8 27B	Lower TTFT (140 ms).
Long-document RAG	Qwen3.8 27B	Larger window (262k).
Screenshots / vision	Qwen3.8 27B	Qwen3.8 27B is the side marked multimodal in the catalog.

Related Editorial Dispatches & Benchmark Notes

View all news

launchAug 14, 2026

Qwen3.8 27B (Aug 14): dense enough to self-host, not a frontier MoE

Alibaba open-weight 27B drop. Local/open-weight lists should see it. It will not win frontier-agents.

launchDec 6, 2024

Llama 3.3 70B stays as the previous Meta 70B workhorse

Dec 2024 instruct 70B. Still a self-host baseline. Llama 4 is the 2026 buy.

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LIVE

Llama 3.3 70B

Elo 1,285

LiveBench49.8%

SWE-bench45.1%

Speed140 tok/s

Output cost$0.32/1M

Alibaba

Qwen3.8 27B

Elo 1,398

LiveBench60.6%

SWE-bench58.8%

Speed152 tok/s

Output cost$3.2/1M

Frequently Asked Questions

Which is better overall, Llama 3.3 70B or Qwen3.8 27B?: Qwen3.8 27B has the higher preference Elo in our latest snapshot (1,398). “Better” still depends on coding, price, and latency — see the table.
Which is better at coding, Llama 3.3 70B or Qwen3.8 27B?: Qwen3.8 27B leads SWE-bench at 58.8% vs 45.1%. SWE-bench is one harness, not your repo.
Which is cheaper to run in production?: Llama 3.3 70B output tokens are $0.32/1M versus $3.2/1M. Input prices and retry rates still move the real bill.
Which is faster, Llama 3.3 70B or Qwen3.8 27B?: Qwen3.8 27B has the lower time-to-first-token (140 ms vs 160 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: Qwen3.8 27B accepts 262k tokens versus 131k.
Can I self-host either model?: Both Llama 3.3 70B and Qwen3.8 27B 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 3.3 70B and Qwen3.8 27B Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All Llama 3.3 70B matchups →|All Qwen3.8 27B matchups →

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

Llama 3.3 70B vs Qwen3.8 27B benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Llama 3.3 70B (2)vsQwen3.8 27B (8)

Llama 3.3 70B: 2W (20%)Overall: Qwen3.8 27BQwen3.8 27B: 8W (80%)

← Llama 3.3 70BQwen3.8 27B →

Qwen3.8 27B (5/5)

Intelligence & Reasoning

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

Preference Elo

1,285vs1,398

Coding Elo

—vs1,422

SWE-bench

45.1%vs58.8%

LiveBench

49.8%vs60.6%

GPQA Diamond

65.7%vs73.4%

Llama 3.3 70B: 0WQwen3.8 27B: 5W

Qwen3.8 27B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

140 tok/svs152 tok/s

Time to first token

160 msvs140 ms

Llama 3.3 70B: 0WQwen3.8 27B: 2W

Llama 3.3 70B (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.32/1Mvs$3.2/1M

Input price

$0.1/1Mvs$0.45/1M

Context window

131kvs262k

Llama 3.3 70B: 2WQwen3.8 27B: 1W

Recommended Workload Routing

Repo / coding agentsQwen3.8 27B

Higher SWE-bench (58.8%).

High-volume chatLlama 3.3 70B

Lower output list price ($0.32/1M).

Voice / low-latency UIQwen3.8 27B

Lower TTFT (140 ms).

Long-document RAGQwen3.8 27B

Larger window (262k).

Screenshots / visionQwen3.8 27B

Qwen3.8 27B is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	Qwen3.8 27B	Higher SWE-bench (58.8%).
High-volume chat	Llama 3.3 70B	Lower output list price ($0.32/1M).
Voice / low-latency UI	Qwen3.8 27B	Lower TTFT (140 ms).
Long-document RAG	Qwen3.8 27B	Larger window (262k).
Screenshots / vision	Qwen3.8 27B	Qwen3.8 27B is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, Llama 3.3 70B or Qwen3.8 27B?

Qwen3.8 27B has the higher preference Elo in our latest snapshot (1,398). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, Llama 3.3 70B or Qwen3.8 27B?

Qwen3.8 27B leads SWE-bench at 58.8% vs 45.1%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

Llama 3.3 70B output tokens are $0.32/1M versus $3.2/1M. Input prices and retry rates still move the real bill.

Which is faster, Llama 3.3 70B or Qwen3.8 27B?

Qwen3.8 27B has the lower time-to-first-token (140 ms vs 160 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

Qwen3.8 27B accepts 262k tokens versus 131k.

Can I self-host either model?

Both Llama 3.3 70B and Qwen3.8 27B 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 3.3 70B and Qwen3.8 27B Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

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Interactive Comparison Diagram

Tug-of-War Matchup Matrix

Intelligence & Reasoning

Speed & Latency

Pricing & Capacity

Multi-Dimensional Capability Radar

Qwen3.8 27B

Qwen3.8 27B

Llama 3.3 70B

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

Qwen3.8 27B (Aug 14): dense enough to self-host, not a frontier MoE

Llama 3.3 70B stays as the previous Meta 70B workhorse

Cast Your Matchup Vote

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CompareLLM Matrix

Llama 3.3 70B

Qwen3.8 27B

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons

Swap or Add Any Model to this Matchup

Interactive Comparison Diagram

Tug-of-War Matchup Matrix

Intelligence & Reasoning

Speed & Latency

Pricing & Capacity

Multi-Dimensional Capability Radar

Qwen3.8 27B

Qwen3.8 27B

Llama 3.3 70B

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

Qwen3.8 27B (Aug 14): dense enough to self-host, not a frontier MoE

Llama 3.3 70B stays as the previous Meta 70B workhorse

Cast Your Matchup Vote

Community Discussions (0)

Share card

CompareLLM Matrix

Llama 3.3 70B

Qwen3.8 27B

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons