LLMBenchmark.ai

Enter opens the highlighted result. Esc closes.

GPT-4o (2024-08-06) vs Qwen3.8 27B

Updated October 21, 2026. 39 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GPT-4o (2024-08-06) Replace Qwen3.8 27B
GPT-4o (2024-08-06)OpenAI
Coding
23.1
Overall
7.7
Output $/M
$10.00
1
Qwen3.8 27BQwen
Coding
73.6
Overall
33.7
Output $/M
$1.35
38
vs boards won of 39 shared

Decision reading

Pick Qwen3.8 27B for coding: it scores 73.6 on the Vibe Coding Index against 23.1 for GPT-4o (2024-08-06).

Pick GPT-4o (2024-08-06) when the benchmark that matters is TaxEval (Vals AI), where it leads.

Model AGPT-4o (2024-08-06)OpenAI23.1/1001 independent benchmarkNo cost per solved taskModel BQwen3.8 27BQwen73.6/1002 independent benchmarks$1.26 per solved task
Shared results
39
GPT-4o (2024-08-06) only
19
Qwen3.8 27B only
46
Like-for-like categories
6 of 9

Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.

Which one for your work

Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.

At a glance

Coding index
GPT-4o (2024-08-06) 23.1 from 1 benchmark, Qwen3.8 27B 73.6 from 2 benchmarks.
Widest category
Coding, 33.4 points apart on average over 2 shared benchmarks.
Widest single gap
LMArena Vision: Chinese: GPT-4o (2024-08-06) 1009 against Qwen3.8 27B 1326, 52.7 points apart on a 0 to 100 scale.
Output price
GPT-4o (2024-08-06) $10.00 against Qwen3.8 27B $1.35 per million output tokens, 7.4x apart.
Context window
GPT-4o (2024-08-06) takes 128K tokens, Qwen3.8 27B 1M.

Benchmark by benchmark

First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.

Boards won, where both have a result
CategoryGPT-4o (2024-08-06)BarsQwen3.8 27BTies
Coding02
Intelligence01
Reasoning06
Math02
Knowledge17
General020
All shared boards138
CodingLike-for-likeFour of these boards feed the Vibe Coding Index02
Coding benchmarks for GPT-4o (2024-08-06) and Qwen3.8 27B
BenchmarkGPT-4o (2024-08-06)Qwen3.8 27BLeader
Aider polyglot23.1%Not measuredOnly GPT-4o (2024-08-06) measured
SWE-bench Lite21.7%Not measuredOnly GPT-4o (2024-08-06) measured
LiveBench codingNot measured86.8%Only Qwen3.8 27B measured
LMArena WebDevNot measured1593Only Qwen3.8 27B measured
AA Coding IndexNot measured68.1Only Qwen3.8 27B measured
LiveCodeBench (Vals AI)Not measured84.0%Only Qwen3.8 27B measured
SWE-bench Verified (Vals AI)Not measured86.0%Only Qwen3.8 27B measured
Vibe Code Bench (Vals AI)Not measured64.8%Only Qwen3.8 27B measured
Code migration (Vals AI)Not measured14.2%Only Qwen3.8 27B measured
ProgramBench (Vals AI)Not measured0.0%Only Qwen3.8 27B measured
IOI (Vals AI)Not measured39.1%Only Qwen3.8 27B measured
Terminal-Bench 2.1 (Vals AI)Not measured58.4%Only Qwen3.8 27B measured
SkillsBench (Vals AI)Not measured38.1%Only Qwen3.8 27B measured
CyberBench (Vals AI)Not measured67.8%Only Qwen3.8 27B measured
LiveCodeBench (Artificial Analysis run)31.7%Not measuredOnly GPT-4o (2024-08-06) measured
SciCodeNot measured46.6%Only Qwen3.8 27B measured
Terminal-Bench Hard (Artificial Analysis run)8.3%Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: coding12831483Qwen3.8 27B
LMArena Text: software and IT12781478Qwen3.8 27B
LMArena WebDev: ReactNot measured1602Only Qwen3.8 27B measured
LMArena WebDev: HTMLNot measured1570Only Qwen3.8 27B measured
LMArena WebDev: from an imageNot measured1577Only Qwen3.8 27B measured
Neither model has a result on the other 16 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GPT-4o (2024-08-06) and Qwen3.8 27B
BenchmarkGPT-4o (2024-08-06)Qwen3.8 27BLeader
AA Intelligence Index7.733.7Qwen3.8 27B
Epoch Capabilities IndexNot measured151.7Only Qwen3.8 27B measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
Agentic work benchmarks for GPT-4o (2024-08-06) and Qwen3.8 27B
BenchmarkGPT-4o (2024-08-06)Qwen3.8 27BLeader
AA Agentic IndexNot measured45.8Only Qwen3.8 27B measured
LiveBench researchNot measured59.4%Only Qwen3.8 27B measured
LiveBench browserNot measured32.0%Only Qwen3.8 27B measured
LiveBench long agenticNot measured27.9%Only Qwen3.8 27B measured
Finance Agent (Vals AI)Not measured48.5%Only Qwen3.8 27B measured
Tax Agent Bench (Vals AI)Not measured30.9%Only Qwen3.8 27B measured
Legal Research Bench (Vals AI)Not measured36.1%Only Qwen3.8 27B measured
Excel Modeling Benchmark (Vals AI)Not measured59.7%Only Qwen3.8 27B measured
Terminal-Bench-Science (Vals AI)Not measured0.0%Only Qwen3.8 27B measured
τ²-Bench Telecom28.9%Not measuredOnly GPT-4o (2024-08-06) measured
APEX-AgentsNot measured47.5%Only Qwen3.8 27B measured
Neither model has a result on the other 28 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
Reasoning benchmarks for GPT-4o (2024-08-06) and Qwen3.8 27B
BenchmarkGPT-4o (2024-08-06)Qwen3.8 27BLeader
GPQA DiamondNot measured88.9%Only Qwen3.8 27B measured
LiveBench reasoningNot measured80.0%Only Qwen3.8 27B measured
MMMU Pro (Vals AI)64.0%83.9%Qwen3.8 27B
GPQA Diamond (Artificial Analysis run)52.1%90.5%Qwen3.8 27B
Humanity's Last Exam (Artificial Analysis run)2.3%33.9%Qwen3.8 27B
GPQA Diamond (Epoch AI run)49.2%Not measuredOnly GPT-4o (2024-08-06) measured
ARC-AGINot measured87.5%Only Qwen3.8 27B measured
ARC-AGI-2Not measured42.4%Only Qwen3.8 27B measured
SimpleBench17.8%Not measuredOnly GPT-4o (2024-08-06) measured
Chess puzzles6.0%Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: hard prompts12641459Qwen3.8 27B
LMArena Text: expert12411479Qwen3.8 27B
LMArena Text: hard prompts in English12771461Qwen3.8 27B
LMArena Vision: diagramsNot measured1285Only Qwen3.8 27B measured
LMArena Vision: homeworkNot measured1295Only Qwen3.8 27B measured
Neither model has a result on the other 6 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for GPT-4o (2024-08-06) and Qwen3.8 27B
BenchmarkGPT-4o (2024-08-06)Qwen3.8 27BLeader
AIME14.0%Not measuredOnly GPT-4o (2024-08-06) measured
MATH 500 (Vals AI)75.2%Not measuredOnly GPT-4o (2024-08-06) measured
MGSM (Vals AI)90.7%Not measuredOnly GPT-4o (2024-08-06) measured
OTIS Mock AIME6.4%Not measuredOnly GPT-4o (2024-08-06) measured
FrontierMath, tiers 1 to 30.4%Not measuredOnly GPT-4o (2024-08-06) measured
ProofBenchNot measured16.0%Only Qwen3.8 27B measured
MATH level 553.3%Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: math12851455Qwen3.8 27B
LMArena Text: mathematical work12831458Qwen3.8 27B
Neither model has a result on the other 4 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index17
Knowledge benchmarks for GPT-4o (2024-08-06) and Qwen3.8 27B
BenchmarkGPT-4o (2024-08-06)Qwen3.8 27BLeader
MMLU Pro74.1%84.3%Qwen3.8 27B
LegalBench (Vals AI)80.1%82.4%Qwen3.8 27B
CorpFin (Vals AI)39.4%Not measuredOnly GPT-4o (2024-08-06) measured
TaxEval (Vals AI)71.1%70.8%GPT-4o (2024-08-06)
MortgageTax (Vals AI)61.0%64.9%Qwen3.8 27B
MedQA (Vals AI)88.2%Not measuredOnly GPT-4o (2024-08-06) measured
MedCode (Vals AI)Not measured28.7%Only Qwen3.8 27B measured
SimpleQA Verified26.0%Not measuredOnly GPT-4o (2024-08-06) measured
MMLU84.3%Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: science12741467Qwen3.8 27B
LMArena Text: medicine and healthcare12521437Qwen3.8 27B
LMArena Text: legal and government12751447Qwen3.8 27B
LMArena Text: business and finance12321438Qwen3.8 27B
Neither model has a result on the other 4 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
Web design benchmarks for GPT-4o (2024-08-06) and Qwen3.8 27B
BenchmarkGPT-4o (2024-08-06)Qwen3.8 27BLeader
LiveBench designNot measured75.9%Only Qwen3.8 27B measured
Neither model has a result on the other 8 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index020
General benchmarks for GPT-4o (2024-08-06) and Qwen3.8 27B
BenchmarkGPT-4o (2024-08-06)Qwen3.8 27BLeader
LiveBench data analysisNot measured76.6%Only Qwen3.8 27B measured
SAGE (Vals AI)Not measured52.4%Only Qwen3.8 27B measured
MedScribe (Vals AI)Not measured83.8%Only Qwen3.8 27B measured
IFBench36.0%Not measuredOnly GPT-4o (2024-08-06) measured
Long context reasoning (AA-LCR)41.0%82.0%Qwen3.8 27B
DTBenchNot measured88.0%Only Qwen3.8 27B measured
LMCANot measured41.4%Only Qwen3.8 27B measured
Surface Evolver BenchNot measured45.0%Only Qwen3.8 27B measured
LMArena Text12831440Qwen3.8 27B
LMArena Text: instruction following12671436Qwen3.8 27B
LMArena Text: creative writing12741383Qwen3.8 27B
LMArena Vision10651269Qwen3.8 27B
LMArena Text: English12971448Qwen3.8 27B
LMArena Text: non-English12621428Qwen3.8 27B
LMArena Text: Chinese12541501Qwen3.8 27B
LMArena Text: German12571443Qwen3.8 27B
LMArena Text: Japanese12341387Qwen3.8 27B
LMArena Text: Korean12181384Qwen3.8 27B
LMArena Text: French12631465Qwen3.8 27B
LMArena Text: Spanish12691449Qwen3.8 27B
LMArena Text: Russian12721415Qwen3.8 27B
LMArena Text: PolishNot measured1439Only Qwen3.8 27B measured
LMArena Text: multi-turn12791440Qwen3.8 27B
LMArena Text: longer queries12831448Qwen3.8 27B
LMArena Text: writing and language12901405Qwen3.8 27B
LMArena Text: entertainment and media12671393Qwen3.8 27B
LMArena Vision: English10721267Qwen3.8 27B
LMArena Vision: Chinese10091326Qwen3.8 27B
LMArena Vision: reading textNot measured1281Only Qwen3.8 27B measured
LMArena Vision: humorNot measured1235Only Qwen3.8 27B measured
LMArena Vision: creative writingNot measured1237Only Qwen3.8 27B measured
Neither model has a result on the other 15 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GPT-4o (2024-08-06) and Qwen3.8 27B
BenchmarkGPT-4o (2024-08-06)Qwen3.8 27BLeader
Neither model has a result on 5 boards here

What each workload costs

Three fixed token mixes at each model's listed price, so per-token rates become a bill.

Chat turn

1K input + 500 output tokens

GPT-4o (2024-08-06)$0.0075Fits in one request
Qwen3.8 27B$0.0007Fits in one request

Qwen3.8 27B costs less

Repository review

50K input + 3K output tokens

GPT-4o (2024-08-06)$0.155Fits in one request
Qwen3.8 27B$0.0060Fits in one request

Qwen3.8 27B costs less

Agent loop

200K input + 10K output tokens

GPT-4o (2024-08-06)$0.600Exceeds the context window
Qwen3.8 27B$0.021Fits in one request

Qwen3.8 27B costs less

Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.

Specification differences

The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.

Specifications of GPT-4o (2024-08-06) and Qwen3.8 27B
SpecificationOpenAI GPT-4o (2024-08-06)Qwen3.8 27B
MakerOpenAIQwen
WeightsNo public weights listedOpen weights
ReleasedAugust 6, 2024August 14, 2026
Context window128K1M
Input $/Mtok$2.50$0.04
Output $/Mtok$10.00$1.35
Coding index23.173.6
Independent measurements behind it12
Cost per solved taskNot computable$1.26
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-4o (2024-08-06) better than Qwen3.8 27B for coding?

Pick Qwen3.8 27B for coding: it scores 73.6 on the Vibe Coding Index against 23.1 for GPT-4o (2024-08-06). Pick GPT-4o (2024-08-06) when the benchmark that matters is TaxEval (Vals AI), where it leads.

Which is cheaper, GPT-4o (2024-08-06) or Qwen3.8 27B?

GPT-4o (2024-08-06) costs $2.50 per million input tokens and $10.00 per million output tokens; Qwen3.8 27B costs $0.04 and $1.35. Cost per solved task is known for Qwen3.8 27B only.

Which has the bigger context window, GPT-4o (2024-08-06) or Qwen3.8 27B?

Qwen3.8 27B, with 1M tokens against 128K.

How many benchmarks is the GPT-4o (2024-08-06) vs Qwen3.8 27B comparison based on?

39 benchmarks have a result for both models as of October 21, 2026. GPT-4o (2024-08-06)'s coding index rests on 1 independent benchmark and Qwen3.8 27B's on 2. Where only one model has a result, the page says so instead of naming a winner.

Compare next