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GPT-4 vs Qwen3.8 27B

Updated October 21, 2026. 34 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GPT-4 Replace Qwen3.8 27B
GPT-4OpenAI
Coding
22.4
Overall
6.7
Output $/M
$60.00
0
Qwen3.8 27BQwen
Coding
73.6
Overall
33.7
Output $/M
$1.35
34
vs boards won of 34 shared

Decision reading

Pick Qwen3.8 27B for coding: it scores 73.6 on the Vibe Coding Index against 22.4 for GPT-4.

GPT-4 is neither cheaper nor larger in context, and leads no benchmark both models were measured on.

Model AGPT-4OpenAI22.4/1001 independent benchmarkNo cost per solved taskModel BQwen3.8 27BQwen73.6/1002 independent benchmarks$1.26 per solved task
Shared results
34
GPT-4 only
11
Qwen3.8 27B only
51
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-4 22.4 from 1 benchmark, Qwen3.8 27B 73.6 from 2 benchmarks.
Widest category
Coding, 49.4 points apart on average over 3 shared benchmarks.
Widest single gap
GPQA Diamond (Artificial Analysis run): GPT-4 34.9% against Qwen3.8 27B 90.5%, 55.6 points apart on a 0 to 100 scale.
Output price
GPT-4 $60.00 against Qwen3.8 27B $1.35 per million output tokens, 44.4x apart.
Context window
GPT-4 takes 8K 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-4BarsQwen3.8 27BTies
Coding03
Intelligence02
Reasoning04
Math02
Knowledge04
General019
All shared boards034
CodingLike-for-likeFour of these boards feed the Vibe Coding Index03
Coding benchmarks for GPT-4 and Qwen3.8 27B
BenchmarkGPT-4Qwen3.8 27BLeader
SWE-bench Verified22.4%Not measuredOnly GPT-4 measured
SWE-bench Lite39.7%Not measuredOnly GPT-4 measured
SWE-bench (full test set)12.5%Not measuredOnly GPT-4 measured
LiveBench codingNot measured86.8%Only Qwen3.8 27B measured
LMArena WebDevNot measured1593Only Qwen3.8 27B measured
AA Coding Index13.168.1Qwen3.8 27B
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
SciCodeNot measured46.6%Only Qwen3.8 27B measured
LMArena Text: coding12091483Qwen3.8 27B
LMArena Text: software and IT11951478Qwen3.8 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 17 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for GPT-4 and Qwen3.8 27B
BenchmarkGPT-4Qwen3.8 27BLeader
AA Intelligence Index6.733.7Qwen3.8 27B
Epoch Capabilities Index125.7151.7Qwen3.8 27B
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
Agentic work benchmarks for GPT-4 and Qwen3.8 27B
BenchmarkGPT-4Qwen3.8 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
APEX-AgentsNot measured47.5%Only Qwen3.8 27B measured
Neither model has a result on the other 29 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index04
Reasoning benchmarks for GPT-4 and Qwen3.8 27B
BenchmarkGPT-4Qwen3.8 27BLeader
GPQA DiamondNot measured88.9%Only Qwen3.8 27B measured
LiveBench reasoningNot measured80.0%Only Qwen3.8 27B measured
MMMU Pro (Vals AI)Not measured83.9%Only Qwen3.8 27B measured
GPQA Diamond (Artificial Analysis run)34.9%90.5%Qwen3.8 27B
Humanity's Last Exam (Artificial Analysis run)Not measured33.9%Only Qwen3.8 27B measured
ARC-AGINot measured87.5%Only Qwen3.8 27B measured
ARC-AGI-2Not measured42.4%Only Qwen3.8 27B measured
BIG-Bench Hard75.1%Not measuredOnly GPT-4 measured
LMArena Text: hard prompts12001459Qwen3.8 27B
LMArena Text: expert11491479Qwen3.8 27B
LMArena Text: hard prompts in English12091461Qwen3.8 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 8 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for GPT-4 and Qwen3.8 27B
BenchmarkGPT-4Qwen3.8 27BLeader
ProofBenchNot measured16.0%Only Qwen3.8 27B measured
GSM8K92.0%Not measuredOnly GPT-4 measured
LMArena Text: math12301455Qwen3.8 27B
LMArena Text: mathematical work12281458Qwen3.8 27B
Neither model has a result on the other 9 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for GPT-4 and Qwen3.8 27B
BenchmarkGPT-4Qwen3.8 27BLeader
MMLU ProNot measured84.3%Only Qwen3.8 27B measured
LegalBench (Vals AI)Not measured82.4%Only Qwen3.8 27B measured
TaxEval (Vals AI)Not measured70.8%Only Qwen3.8 27B measured
MortgageTax (Vals AI)Not measured64.9%Only Qwen3.8 27B measured
MedCode (Vals AI)Not measured28.7%Only Qwen3.8 27B measured
MMLU Pro (Artificial Analysis run)56.2%Not measuredOnly GPT-4 measured
MMLU86.4%Not measuredOnly GPT-4 measured
AI2 Reasoning Challenge96.3%Not measuredOnly GPT-4 measured
LMArena Text: science11771467Qwen3.8 27B
LMArena Text: medicine and healthcare11451437Qwen3.8 27B
LMArena Text: legal and government11911447Qwen3.8 27B
LMArena Text: business and finance11451438Qwen3.8 27B
Neither model has a result on the other 5 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
Web design benchmarks for GPT-4 and Qwen3.8 27B
BenchmarkGPT-4Qwen3.8 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 index019
General benchmarks for GPT-4 and Qwen3.8 27B
BenchmarkGPT-4Qwen3.8 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
IFBench33.2%Not measuredOnly GPT-4 measured
Long context reasoning (AA-LCR)Not measured82.0%Only Qwen3.8 27B measured
DTBench62.7%88.0%Qwen3.8 27B
LMCA17.1%41.4%Qwen3.8 27B
Surface Evolver BenchNot measured45.0%Only Qwen3.8 27B measured
HellaSwag95.3%Not measuredOnly GPT-4 measured
WinoGrande87.5%Not measuredOnly GPT-4 measured
LMArena Text12061440Qwen3.8 27B
LMArena Text: instruction following12011436Qwen3.8 27B
LMArena Text: creative writing11911383Qwen3.8 27B
LMArena VisionNot measured1269Only Qwen3.8 27B measured
LMArena Text: English12201448Qwen3.8 27B
LMArena Text: non-English11941428Qwen3.8 27B
LMArena Text: Chinese11841501Qwen3.8 27B
LMArena Text: German11981443Qwen3.8 27B
LMArena Text: Japanese11371387Qwen3.8 27B
LMArena Text: Korean10871384Qwen3.8 27B
LMArena Text: French12201465Qwen3.8 27B
LMArena Text: Spanish11971449Qwen3.8 27B
LMArena Text: Russian11951415Qwen3.8 27B
LMArena Text: Polish12651439Qwen3.8 27B
LMArena Text: multi-turn12061440Qwen3.8 27B
LMArena Text: longer queries11901448Qwen3.8 27B
LMArena Text: writing and language12191405Qwen3.8 27B
LMArena Text: entertainment and media11841393Qwen3.8 27B
LMArena Vision: EnglishNot measured1267Only Qwen3.8 27B measured
LMArena Vision: ChineseNot measured1326Only Qwen3.8 27B measured
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 13 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GPT-4 and Qwen3.8 27B
BenchmarkGPT-4Qwen3.8 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-4$0.060Fits in one request
Qwen3.8 27B$0.0007Fits in one request

Qwen3.8 27B costs less

Repository review

50K input + 3K output tokens

GPT-4$1.68Exceeds the context window
Qwen3.8 27B$0.0060Fits in one request

Qwen3.8 27B costs less

Agent loop

200K input + 10K output tokens

GPT-4$6.60Exceeds 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-4 and Qwen3.8 27B
SpecificationOpenAI GPT-4Qwen3.8 27B
MakerOpenAIQwen
WeightsNo public weights listedOpen weights
ReleasedMay 28, 2023August 14, 2026
Context window8K1M
Input $/Mtok$30.00$0.04
Output $/Mtok$60.00$1.35
Coding index22.473.6
Independent measurements behind it12
Cost per solved taskNot computable$1.26
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-4 better than Qwen3.8 27B for coding?

Pick Qwen3.8 27B for coding: it scores 73.6 on the Vibe Coding Index against 22.4 for GPT-4. GPT-4 is neither cheaper nor larger in context, and leads no benchmark both models were measured on.

Which is cheaper, GPT-4 or Qwen3.8 27B?

GPT-4 costs $30.00 per million input tokens and $60.00 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-4 or Qwen3.8 27B?

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

How many benchmarks is the GPT-4 vs Qwen3.8 27B comparison based on?

34 benchmarks have a result for both models as of October 21, 2026. GPT-4'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.

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