Pairwise benchmark snapshot · Aug 16, 2026

GPT-5 vs Qwen3.8 27B benchmark

Name: GPT-5 vs Qwen3.8 27B AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares GPT-5 (OpenAI) and Qwen3.8 27B (Alibaba) using the latest snapshots we have as of Aug 16, 2026. On preference Elo, GPT-5 is ahead by 160 points (1,558 vs 1,398; seed-bootstrap (Aug 1, 2026)). On SWE-bench coding, GPT-5 resolves 68.4% versus 58.8% (seed-bootstrap (Aug 1, 2026)). Qwen3.8 27B streams faster (152 tok/s) while Qwen3.8 27B is the cheaper output token ($3.2/1M). GPT-5 has the larger context window (400k 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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OpenAI

GPT-5

OpenAI flagship reasoning model for 2025–26. Strong general preference Elo and multimodal coverage.

Elo 1,558$10/1M out

Alibaba

Qwen3.8 27B

Alibaba open-weight 27B drop dated Aug 14 2026. Dense enough to self-host; not a frontier MoE.

Elo 1,398$3.1999999999999997/1M 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.

GPT-5 (6)vsQwen3.8 27B (4)

GPT-5: 6W (60%)Overall: GPT-5Qwen3.8 27B: 4W (40%)

← GPT-5Qwen3.8 27B →

GPT-5 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,558vs1,398

Coding Elo

1,540vs1,422

SWE-bench

68.4%vs58.8%

LiveBench

69.8%vs60.6%

GPQA Diamond

85.2%vs73.4%

GPT-5: 5WQwen3.8 27B: 0W

Qwen3.8 27B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

78 tok/svs152 tok/s

Time to first token

290 msvs140 ms

GPT-5: 0WQwen3.8 27B: 2W

Qwen3.8 27B (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$10/1Mvs$3.2/1M

Input price

$1.25/1Mvs$0.45/1M

Context window

400kvs262k

GPT-5: 1WQwen3.8 27B: 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 AdvantageGPT-5 4/6

Preference Elo

GPT-5

160 pts advantage

Throughput Speed

Qwen3.8 27B

74 tok/s faster

Price Efficiency

Qwen3.8 27B

$6.80/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

+11%1,558

EloHigh is better

1,398

Code Elo

High is better

+8%1,540

Code EloHigh is better

1,422

LiveBench

High is better

+15%69.8%

LiveBenchHigh is better

60.6%

SWE-bench

High is better

+16%68.4%

SWE-benchHigh is better

58.8%

GPQA

High is better

+16%85.2%

GPQAHigh is better

73.4%

TTFT

Low is better

290 ms

TTFTLow is better

140 ms+107%

Speed

High is better

78 tok/s

SpeedHigh is better

152 tok/s+95%

In $

Low is better

$1.25/1M

In $Low is better

$0.45/1M+178%

Out $

Low is better

$10/1M

Out $Low is better

$3.2/1M+213%

Context

High is better

+53%400k

ContextHigh is better

262k

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 67% with Qwen3.8 27B

Monthly Volume50M tokens/mo

Quick Presets:

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

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

GPT-5$193.75 / mo

In: $43.75Out: $150.00

Qwen3.8 27B$63.75 / mo

In: $15.75Out: $48.00

Estimated Cost Delta

Qwen3.8 27B is estimated to save $130.00/month ($1,560/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
GPT-5:Pick GPT-5 when repo-level coding accuracy is the constraint.
2
Qwen3.8 27B:Pick Qwen3.8 27B 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 agentsGPT-5

Higher SWE-bench (68.4%).

High-volume chatQwen3.8 27B

Lower output list price ($3.2/1M).

Voice / low-latency UIQwen3.8 27B

Lower TTFT (140 ms).

Long-document RAGGPT-5

Larger window (400k).

Screenshots / visionGPT-5

GPT-5 is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	GPT-5	Higher SWE-bench (68.4%).
High-volume chat	Qwen3.8 27B	Lower output list price ($3.2/1M).
Voice / low-latency UI	Qwen3.8 27B	Lower TTFT (140 ms).
Long-document RAG	GPT-5	Larger window (400k).
Screenshots / vision	GPT-5	GPT-5 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.

launchAug 8, 2025

GPT-5 (2025) remains the legacy OpenAI flagship baseline

Still a common compare target. 5.6 Sol replaced it as the buy; GPT-5 stays for old URLs.

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LIVE

OpenAI

GPT-5

Elo 1,558

LiveBench69.8%

SWE-bench68.4%

Speed78 tok/s

Output cost$10/1M

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, GPT-5 or Qwen3.8 27B?: GPT-5 has the higher preference Elo in our latest snapshot (1,558). “Better” still depends on coding, price, and latency — see the table.
Which is better at coding, GPT-5 or Qwen3.8 27B?: GPT-5 leads SWE-bench at 68.4% vs 58.8%. SWE-bench is one harness, not your repo.
Which is cheaper to run in production?: Qwen3.8 27B output tokens are $3.2/1M versus $10/1M. Input prices and retry rates still move the real bill.
Which is faster, GPT-5 or Qwen3.8 27B?: Qwen3.8 27B has the lower time-to-first-token (140 ms vs 290 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: GPT-5 accepts 400k tokens versus 262k.
Can I self-host either model?: Qwen3.8 27B 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. GPT-5 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 GPT-5 matchups →|All Qwen3.8 27B matchups →

GPT-5 vs GPT-4.5 Orion (previous gpt-flagship)GPT-5 vs GPT-5.6 Sol (next gpt-flagship)Qwen3.8 27B vs Qwen 3 Max (previous qwen)GPT-5.6 Terra vs GPT-5 Qwen 3 Max vs GPT-5 Qwen QwQ 32B vs GPT-5

GPT-5 vs Qwen3.8 27B benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

GPT-5 (6)vsQwen3.8 27B (4)

GPT-5: 6W (60%)Overall: GPT-5Qwen3.8 27B: 4W (40%)

← GPT-5Qwen3.8 27B →

GPT-5 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,558vs1,398

Coding Elo

1,540vs1,422

SWE-bench

68.4%vs58.8%

LiveBench

69.8%vs60.6%

GPQA Diamond

85.2%vs73.4%

GPT-5: 5WQwen3.8 27B: 0W

Qwen3.8 27B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

78 tok/svs152 tok/s

Time to first token

290 msvs140 ms

GPT-5: 0WQwen3.8 27B: 2W

Qwen3.8 27B (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$10/1Mvs$3.2/1M

Input price

$1.25/1Mvs$0.45/1M

Context window

400kvs262k

GPT-5: 1WQwen3.8 27B: 2W

Recommended Workload Routing

Repo / coding agentsGPT-5

Higher SWE-bench (68.4%).

High-volume chatQwen3.8 27B

Lower output list price ($3.2/1M).

Voice / low-latency UIQwen3.8 27B

Lower TTFT (140 ms).

Long-document RAGGPT-5

Larger window (400k).

Screenshots / visionGPT-5

GPT-5 is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	GPT-5	Higher SWE-bench (68.4%).
High-volume chat	Qwen3.8 27B	Lower output list price ($3.2/1M).
Voice / low-latency UI	Qwen3.8 27B	Lower TTFT (140 ms).
Long-document RAG	GPT-5	Larger window (400k).
Screenshots / vision	GPT-5	GPT-5 is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, GPT-5 or Qwen3.8 27B?

GPT-5 has the higher preference Elo in our latest snapshot (1,558). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, GPT-5 or Qwen3.8 27B?

GPT-5 leads SWE-bench at 68.4% vs 58.8%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

Qwen3.8 27B output tokens are $3.2/1M versus $10/1M. Input prices and retry rates still move the real bill.

Which is faster, GPT-5 or Qwen3.8 27B?

Qwen3.8 27B has the lower time-to-first-token (140 ms vs 290 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

GPT-5 accepts 400k tokens versus 262k.

Can I self-host either model?

Qwen3.8 27B 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. GPT-5 and Qwen3.8 27B Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

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

GPT-5

Qwen3.8 27B

Qwen3.8 27B

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

GPT-5 (2025) remains the legacy OpenAI flagship baseline

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CompareLLM Matrix

GPT-5

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

GPT-5

Qwen3.8 27B

Qwen3.8 27B

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

GPT-5 (2025) remains the legacy OpenAI flagship baseline

Cast Your Matchup Vote

Community Discussions (0)

Share card

CompareLLM Matrix

GPT-5

Qwen3.8 27B

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons