Enter opens the highlighted result. Esc closes.

Gemini 2.5 Pro vs Qwen3.8 27B

Qwen3.8 27B leads Gemini 2.5 Pro on coding, 74.2 to 19.1 on the Vibe Coding Index, and Qwen3.8 27B costs 3.9x less per output token ($2.55 vs $10.00 per million). Updated October 22, 2026; 57 benchmarks have a result for both.

Replace Gemini 2.5 Pro Replace Qwen3.8 27B
Gemini 2.5 ProGoogle
Coding
19.1
Overall
41.8
Output $/M
$10.00
25
Qwen3.8 27BQwen
Coding
74.2
Overall
59.6
Output $/M
$2.55
32
vs boards won of 57 shared

Gemini 2.5 Pro or Qwen3.8 27B: which to pick

Pick Qwen3.8 27B for coding: it scores 74.2 on the Vibe Coding Index against 19.1 for Gemini 2.5 Pro.

Pick Gemini 2.5 Pro for a lower cost per solved task ($0.64 against $1.26) and a larger context window (1.05M against 1M tokens).

Model AGemini 2.5 ProGoogle19.1/1003 independent benchmarks$0.64 per solved taskModel BQwen3.8 27BQwen74.2/1004 independent benchmarks$1.26 per solved task
Shared results
57
Gemini 2.5 Pro only
33
Qwen3.8 27B only
28
Like-for-like categories
7 of 9

Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.

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
Gemini 2.5 Pro 19.1 from 3 benchmarks, Qwen3.8 27B 74.2 from 4 benchmarks.
Widest category
Agentic work, 44.2 points apart on average over 1 shared benchmark.
Widest single gap
Vibe Code Bench (Vals AI): Gemini 2.5 Pro 0.4% against Qwen3.8 27B 64.8%, 64.4 points apart on a 0 to 100 scale.
Output price
Gemini 2.5 Pro $10.00 against Qwen3.8 27B $2.55 per million output tokens, 3.9x apart.
Context window
Gemini 2.5 Pro takes 1.05M 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
CategoryGemini 2.5 ProBarsQwen3.8 27BTies
Coding09
Intelligence02
Agentic work01
Reasoning09
Math02
Knowledge60
General199
All shared boards2532
CodingLike-for-likeSix of these boards feed the Vibe Coding Index09
Coding benchmarks for Gemini 2.5 Pro and Qwen3.8 27B
BenchmarkGemini 2.5 ProQwen3.8 27BLeader
SWE-bench Verified53.6%Not measuredOnly Gemini 2.5 Pro measured
LiveBench codingNot measured86.8%Only Qwen3.8 27B measured
LMArena WebDev12261593Qwen3.8 27B
AA Coding Index33.368.1Qwen3.8 27B
LiveCodeBench (Vals AI)Not measured84.0%Only Qwen3.8 27B measured
SWE-bench Verified (Vals AI)54.4%86.0%Qwen3.8 27B
Vibe Code Bench (Vals AI)0.4%64.8%Qwen3.8 27B
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)80.1%Not measuredOnly Gemini 2.5 Pro measured
SciCode46.3%46.6%Qwen3.8 27B
Terminal-Bench Hard (Artificial Analysis run)26.5%Not measuredOnly Gemini 2.5 Pro measured
Terminal-Bench32.6%Not measuredOnly Gemini 2.5 Pro measured
GSO-Bench3.9%Not measuredOnly Gemini 2.5 Pro measured
WeirdML54.0%Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: coding14521483Qwen3.8 27B
LMArena Text: software and IT14591478Qwen3.8 27B
LMArena WebDev: ReactNot measured1602Only Qwen3.8 27B measured
LMArena WebDev: HTML12351570Qwen3.8 27B
LMArena WebDev: from an image12851577Qwen3.8 27B
Neither model has a result on the other 15 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for Gemini 2.5 Pro and Qwen3.8 27B
BenchmarkGemini 2.5 ProQwen3.8 27BLeader
AA Intelligence Index16.133.7Qwen3.8 27B
Epoch Capabilities Index145.1151.7Qwen3.8 27B
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for Gemini 2.5 Pro and Qwen3.8 27B
BenchmarkGemini 2.5 ProQwen3.8 27BLeader
AA Agentic Index1.645.8Qwen3.8 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 Telecom54.1%Not measuredOnly Gemini 2.5 Pro measured
APEX-AgentsNot measured47.5%Only Qwen3.8 27B measured
GDPval23.3%Not measuredOnly Gemini 2.5 Pro measured
DeepResearch Bench41.5%Not measuredOnly Gemini 2.5 Pro measured
Vending-Bench 2$574Not measuredOnly Gemini 2.5 Pro measured
BrowseComp-Plus29.5%Not measuredOnly Gemini 2.5 Pro measured
Neither model has a result on the other 26 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index09
Reasoning benchmarks for Gemini 2.5 Pro and Qwen3.8 27B
BenchmarkGemini 2.5 ProQwen3.8 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)84.4%90.5%Qwen3.8 27B
Humanity's Last Exam (Artificial Analysis run)22.5%33.9%Qwen3.8 27B
GPQA Diamond (Epoch AI run)85.3%Not measuredOnly Gemini 2.5 Pro measured
ARC-AGI41.0%87.5%Qwen3.8 27B
ARC-AGI-24.9%42.4%Qwen3.8 27B
LMArena Text: hard prompts14551459Qwen3.8 27B
LMArena Text: expert14521479Qwen3.8 27B
LMArena Text: hard prompts in English14531461Qwen3.8 27B
LMArena Vision: diagrams12741285Qwen3.8 27B
LMArena Vision: homework12811295Qwen3.8 27B
Kagi LLM Benchmark70.3%Not measuredOnly Gemini 2.5 Pro 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 Gemini 2.5 Pro and Qwen3.8 27B
BenchmarkGemini 2.5 ProQwen3.8 27BLeader
AA Math Index87.7Not measuredOnly Gemini 2.5 Pro measured
AIME 2025 (Artificial Analysis run)87.7%Not measuredOnly Gemini 2.5 Pro measured
OTIS Mock AIME84.7%Not measuredOnly Gemini 2.5 Pro measured
FrontierMath, tiers 1 to 324.6%Not measuredOnly Gemini 2.5 Pro measured
FrontierMath, tier 40.0%Not measuredOnly Gemini 2.5 Pro measured
ProofBenchNot measured16.0%Only Qwen3.8 27B measured
LMArena Text: math14501455Qwen3.8 27B
LMArena Text: mathematical work14541458Qwen3.8 27B
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index60
Knowledge benchmarks for Gemini 2.5 Pro and Qwen3.8 27B
BenchmarkGemini 2.5 ProQwen3.8 27BLeader
MMLU ProNot measured84.3%Only Qwen3.8 27B measured
LegalBench (Vals AI)Not measured82.4%Only Qwen3.8 27B measured
CaseLaw (Vals AI)63.9%Not measuredOnly Gemini 2.5 Pro measured
CorpFin (Vals AI)60.8%Not measuredOnly Gemini 2.5 Pro measured
TaxEval (Vals AI)Not measured70.8%Only Qwen3.8 27B measured
MortgageTax (Vals AI)68.9%64.9%Gemini 2.5 Pro
MedCode (Vals AI)50.6%28.7%Gemini 2.5 Pro
MMLU Pro (Artificial Analysis run)86.2%Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: science14801467Gemini 2.5 Pro
LMArena Text: medicine and healthcare14761437Gemini 2.5 Pro
LMArena Text: legal and government14801447Gemini 2.5 Pro
LMArena Text: business and finance14391438Gemini 2.5 Pro
FACTS Benchmark Suite61.5%Not measuredOnly Gemini 2.5 Pro measured
Neither model has a result on the other 5 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Web design benchmarks for Gemini 2.5 Pro and Qwen3.8 27B
BenchmarkGemini 2.5 ProQwen3.8 27BLeader
Design Arena: websites1167Not measuredOnly Gemini 2.5 Pro measured
Design Arena: UI components1137Not measuredOnly Gemini 2.5 Pro measured
Design Arena: game dev1111Not measuredOnly Gemini 2.5 Pro measured
Design Arena: data viz1222Not measuredOnly Gemini 2.5 Pro measured
Design Arena: 3D1080Not measuredOnly Gemini 2.5 Pro measured
Design Arena: all code categories1152Not measuredOnly Gemini 2.5 Pro measured
LiveBench designNot measured75.9%Only Qwen3.8 27B measured
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index199
General benchmarks for Gemini 2.5 Pro and Qwen3.8 27B
BenchmarkGemini 2.5 ProQwen3.8 27BLeader
LiveBench data analysisNot measured76.6%Only Qwen3.8 27B measured
SAGE (Vals AI)41.9%52.4%Qwen3.8 27B
MedScribe (Vals AI)73.5%83.8%Qwen3.8 27B
IFBench48.7%Not measuredOnly Gemini 2.5 Pro measured
Long context reasoning (AA-LCR)69.0%82.0%Qwen3.8 27B
DTBench82.4%88.0%Qwen3.8 27B
LMCA34.8%41.4%Qwen3.8 27B
Creative writing (Lech Mazur)83.8%Not measuredOnly Gemini 2.5 Pro measured
Surface Evolver BenchNot measured45.0%Only Qwen3.8 27B measured
LMArena Text14581440Gemini 2.5 Pro
LMArena Text: instruction following14371436Gemini 2.5 Pro
LMArena Text: creative writing14541383Gemini 2.5 Pro
LMArena Vision12631269Qwen3.8 27B
LMArena Text: English14581448Gemini 2.5 Pro
LMArena Text: non-English14521428Gemini 2.5 Pro
LMArena Text: Chinese15071501Gemini 2.5 Pro
LMArena Text: German14871443Gemini 2.5 Pro
LMArena Text: Japanese14611387Gemini 2.5 Pro
LMArena Text: Korean14341384Gemini 2.5 Pro
LMArena Text: French14731465Gemini 2.5 Pro
LMArena Text: Spanish14741449Gemini 2.5 Pro
LMArena Text: Russian14611415Gemini 2.5 Pro
LMArena Text: Polish14791439Gemini 2.5 Pro
LMArena Text: multi-turn14531440Gemini 2.5 Pro
LMArena Text: longer queries14491448Gemini 2.5 Pro
LMArena Text: writing and language14541405Gemini 2.5 Pro
LMArena Text: entertainment and media14411393Gemini 2.5 Pro
LMArena Vision: English12611267Qwen3.8 27B
LMArena Vision: Chinese13001326Qwen3.8 27B
LMArena Vision: reading text12701281Qwen3.8 27B
LMArena Vision: humor12621235Gemini 2.5 Pro
LMArena Vision: captioning1304Not measuredOnly Gemini 2.5 Pro measured
LMArena Vision: recognizing things1283Not measuredOnly Gemini 2.5 Pro measured
LMArena Vision: creative writing12651237Gemini 2.5 Pro
LMArena Document1421Not measuredOnly Gemini 2.5 Pro measured
Neither model has a result on the other 11 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Gemini 2.5 Pro and Qwen3.8 27B
BenchmarkGemini 2.5 ProQwen3.8 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

Gemini 2.5 Pro$0.0063Fits in one request
Qwen3.8 27B$0.0017Fits in one request

Qwen3.8 27B costs less

Repository review

50K input + 3K output tokens

Gemini 2.5 Pro$0.092Fits in one request
Qwen3.8 27B$0.029Fits in one request

Qwen3.8 27B costs less

Agent loop

200K input + 10K output tokens

Gemini 2.5 Pro$0.350Fits in one request
Qwen3.8 27B$0.111Fits 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 Gemini 2.5 Pro and Qwen3.8 27B
SpecificationGoogle Gemini 2.5 ProQwen3.8 27B
MakerGoogleQwen
WeightsNo public weights listedOpen weights
ReleasedJune 17, 2025August 14, 2026
Context window1.05M1M
Input $/Mtok$1.25$0.425
Output $/Mtok$10.00$2.55
Coding index19.174.2
Independent measurements behind it34
Cost per solved task$0.64$1.26
SourceOpenRouter listingOpenRouter listing

Questions

Is Gemini 2.5 Pro better than Qwen3.8 27B for coding?

Pick Qwen3.8 27B for coding: it scores 74.2 on the Vibe Coding Index against 19.1 for Gemini 2.5 Pro. Pick Gemini 2.5 Pro for a lower cost per solved task ($0.64 against $1.26) and a larger context window (1.05M against 1M tokens).

Which is cheaper, Gemini 2.5 Pro or Qwen3.8 27B?

Gemini 2.5 Pro costs $1.25 per million input tokens and $10.00 per million output tokens; Qwen3.8 27B costs $0.425 and $2.55. Per solved SWE-bench Verified task, Gemini 2.5 Pro costs $0.64 and Qwen3.8 27B $1.26.

Which has the bigger context window, Gemini 2.5 Pro or Qwen3.8 27B?

Gemini 2.5 Pro, with 1.05M tokens against 1M.

How many benchmarks is the Gemini 2.5 Pro vs Qwen3.8 27B comparison based on?

57 benchmarks have a result for both models as of October 22, 2026. Gemini 2.5 Pro's coding index rests on 3 independent benchmarks and Qwen3.8 27B's on 4. Where only one model has a result, the page says so instead of naming a winner.

Compare next

Embed this comparison on your site

Free under CC BY 4.0, while the source link stays visible. Preview the frame · Terms and other embeds

<iframe src="https://llmbenchmark.ai/embed/compare/gemini-2-5-pro-vs-qwen3-8-27b" title="Gemini 2.5 Pro vs Qwen3.8 27B, LLMBenchmark.ai" width="100%" height="360" style="border:0;max-width:100%" loading="lazy"></iframe>
<p style="font:13px/1.4 sans-serif;margin:6px 0 0">Source: <a href="https://llmbenchmark.ai/compare/gemini-2-5-pro-vs-qwen3-8-27b">LLMBenchmark.ai, Gemini 2.5 Pro vs Qwen3.8 27B</a></p>