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Gemma 4 26B A4B vs Qwen3.8 27B

Updated October 21, 2026. 45 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace Gemma 4 26B A4B Replace Qwen3.8 27B
Gemma 4 26B A4BGoogle
Coding
22.8
Overall
16.7
Output $/M
$0.255
9
Qwen3.8 27BQwen
Coding
73.6
Overall
33.7
Output $/M
$1.35
36
vs boards won of 45 shared

Decision reading

Pick Qwen3.8 27B for coding: it scores 73.6 on the Vibe Coding Index against 22.8 for Gemma 4 26B A4B.

Pick Gemma 4 26B A4B when output tokens cost 5.3x less, or when what matters is LMArena Text: math or LMArena Text: mathematical work, where it leads.

Model AGemma 4 26B A4BGoogle22.8/1001 independent benchmarkNo cost per solved taskModel BQwen3.8 27BQwen73.6/1002 independent benchmarks$1.26 per solved task
Shared results
45
Gemma 4 26B A4B only
9
Qwen3.8 27B only
40
Like-for-like categories
7 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
Gemma 4 26B A4B 22.8 from 1 benchmark, Qwen3.8 27B 73.6 from 2 benchmarks.
Widest category
Agentic work, 42.9 points apart on average over 1 shared benchmark.
Widest single gap
AA Agentic Index: Gemma 4 26B A4B 2.9 against Qwen3.8 27B 45.8, 42.9 points apart on a 0 to 100 scale.
Output price
Gemma 4 26B A4B $0.255 against Qwen3.8 27B $1.35 per million output tokens, 5.3x apart.
Context window
Gemma 4 26B A4B takes 262K 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
CategoryGemma 4 26B A4BBarsQwen3.8 27BTies
Coding07
Intelligence02
Agentic work01
Reasoning07
Math20
Knowledge13
General616
All shared boards936
CodingLike-for-likeFour of these boards feed the Vibe Coding Index07
Coding benchmarks for Gemma 4 26B A4B and Qwen3.8 27B
BenchmarkGemma 4 26B A4BQwen3.8 27BLeader
LiveBench codingNot measured86.8%Only Qwen3.8 27B measured
LMArena WebDev13601593Qwen3.8 27B
AA Coding Index39.368.1Qwen3.8 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
SciCode40.0%46.6%Qwen3.8 27B
Terminal-Bench Hard (Artificial Analysis run)25.0%Not measuredOnly Gemma 4 26B A4B measured
WeirdML35.2%Not measuredOnly Gemma 4 26B A4B measured
ALE-Bench927.2Not measuredOnly Gemma 4 26B A4B measured
LMArena Text: coding14461483Qwen3.8 27B
LMArena Text: software and IT14541478Qwen3.8 27B
LMArena WebDev: React13511602Qwen3.8 27B
LMArena WebDev: HTML13621570Qwen3.8 27B
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 Gemma 4 26B A4B and Qwen3.8 27B
BenchmarkGemma 4 26B A4BQwen3.8 27BLeader
AA Intelligence Index16.733.7Qwen3.8 27B
Epoch Capabilities Index141.9151.7Qwen3.8 27B
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for Gemma 4 26B A4B and Qwen3.8 27B
BenchmarkGemma 4 26B A4BQwen3.8 27BLeader
AA Agentic Index2.945.8Qwen3.8 27B
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 Telecom43.6%Not measuredOnly Gemma 4 26B A4B 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 index07
Reasoning benchmarks for Gemma 4 26B A4B and Qwen3.8 27B
BenchmarkGemma 4 26B A4BQwen3.8 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)79.2%90.5%Qwen3.8 27B
Humanity's Last Exam (Artificial Analysis run)19.3%33.9%Qwen3.8 27B
GPQA Diamond (Epoch AI run)73.2%Not measuredOnly Gemma 4 26B A4B measured
ARC-AGINot measured87.5%Only Qwen3.8 27B measured
ARC-AGI-2Not measured42.4%Only Qwen3.8 27B measured
LMArena Text: hard prompts14381459Qwen3.8 27B
LMArena Text: expert14471479Qwen3.8 27B
LMArena Text: hard prompts in English14481461Qwen3.8 27B
LMArena Vision: diagrams12641285Qwen3.8 27B
LMArena Vision: homework12931295Qwen3.8 27B
Neither model has a result on the other 8 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index20
Math benchmarks for Gemma 4 26B A4B and Qwen3.8 27B
BenchmarkGemma 4 26B A4BQwen3.8 27BLeader
OTIS Mock AIME82.2%Not measuredOnly Gemma 4 26B A4B measured
ProofBenchNot measured16.0%Only Qwen3.8 27B measured
LMArena Text: math14701455Gemma 4 26B A4B
LMArena Text: mathematical work14751458Gemma 4 26B A4B
Neither model has a result on the other 9 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index13
Knowledge benchmarks for Gemma 4 26B A4B and Qwen3.8 27B
BenchmarkGemma 4 26B A4BQwen3.8 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
LMArena Text: science14461467Qwen3.8 27B
LMArena Text: medicine and healthcare14281437Qwen3.8 27B
LMArena Text: legal and government14571447Gemma 4 26B A4B
LMArena Text: business and finance14291438Qwen3.8 27B
Neither model has a result on the other 8 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
Web design benchmarks for Gemma 4 26B A4B and Qwen3.8 27B
BenchmarkGemma 4 26B A4BQwen3.8 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 index616
General benchmarks for Gemma 4 26B A4B and Qwen3.8 27B
BenchmarkGemma 4 26B A4BQwen3.8 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
IFBench72.4%Not measuredOnly Gemma 4 26B A4B measured
Long context reasoning (AA-LCR)65.7%82.0%Qwen3.8 27B
DTBench74.9%88.0%Qwen3.8 27B
LMCA29.7%41.4%Qwen3.8 27B
Surface Evolver BenchNot measured45.0%Only Qwen3.8 27B measured
LMArena Text14341440Qwen3.8 27B
LMArena Text: instruction following14201436Qwen3.8 27B
LMArena Text: creative writing14001383Gemma 4 26B A4B
LMArena Vision12601269Qwen3.8 27B
LMArena Text: English14441448Qwen3.8 27B
LMArena Text: non-English14211428Qwen3.8 27B
LMArena Text: Chinese14951501Qwen3.8 27B
LMArena Text: GermanNot measured1443Only Qwen3.8 27B measured
LMArena Text: JapaneseNot measured1387Only Qwen3.8 27B measured
LMArena Text: KoreanNot measured1384Only Qwen3.8 27B measured
LMArena Text: French14601465Qwen3.8 27B
LMArena Text: Spanish14151449Qwen3.8 27B
LMArena Text: Russian14341415Gemma 4 26B A4B
LMArena Text: PolishNot measured1439Only Qwen3.8 27B measured
LMArena Text: multi-turn14411440Gemma 4 26B A4B
LMArena Text: longer queries14271448Qwen3.8 27B
LMArena Text: writing and language14051405Qwen3.8 27B
LMArena Text: entertainment and media14031393Gemma 4 26B A4B
LMArena Vision: English12561267Qwen3.8 27B
LMArena Vision: Chinese13121326Qwen3.8 27B
LMArena Vision: reading text12681281Qwen3.8 27B
LMArena Vision: humor12721235Gemma 4 26B A4B
LMArena Vision: captioning1297Not measuredOnly Gemma 4 26B A4B measured
LMArena Vision: recognizing things1191Not measuredOnly Gemma 4 26B A4B measured
LMArena Vision: creative writing12561237Gemma 4 26B A4B
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 Gemma 4 26B A4B and Qwen3.8 27B
BenchmarkGemma 4 26B A4BQwen3.8 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

Gemma 4 26B A4B$0.0002Fits in one request
Qwen3.8 27B$0.0007Fits in one request

Gemma 4 26B A4B costs less

Repository review

50K input + 3K output tokens

Gemma 4 26B A4B$0.0046Fits in one request
Qwen3.8 27B$0.0060Fits in one request

Gemma 4 26B A4B costs less

Agent loop

200K input + 10K output tokens

Gemma 4 26B A4B$0.018Fits in one request
Qwen3.8 27B$0.021Fits in one request

Gemma 4 26B A4B 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 Gemma 4 26B A4B and Qwen3.8 27B
SpecificationGoogle Gemma 4 26B A4BQwen3.8 27B
MakerGoogleQwen
WeightsOpen weightsOpen weights
ReleasedApril 3, 2026August 14, 2026
Context window262K1M
Input $/Mtok$0.0765$0.04
Output $/Mtok$0.255$1.35
Coding index22.873.6
Independent measurements behind it12
Cost per solved taskNot computable$1.26
SourceOpenRouter listingOpenRouter listing

Questions

Is Gemma 4 26B A4B better than Qwen3.8 27B for coding?

Pick Qwen3.8 27B for coding: it scores 73.6 on the Vibe Coding Index against 22.8 for Gemma 4 26B A4B. Pick Gemma 4 26B A4B when output tokens cost 5.3x less, or when what matters is LMArena Text: math or LMArena Text: mathematical work, where it leads.

Which is cheaper, Gemma 4 26B A4B or Qwen3.8 27B?

Gemma 4 26B A4B costs $0.0765 per million input tokens and $0.255 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, Gemma 4 26B A4B or Qwen3.8 27B?

Qwen3.8 27B, with 1M tokens against 262K.

How many benchmarks is the Gemma 4 26B A4B vs Qwen3.8 27B comparison based on?

45 benchmarks have a result for both models as of October 21, 2026. Gemma 4 26B A4B'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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