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Gemini 2.5 Pro vs Sonar Reasoning Pro

Updated October 21, 2026. 1 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Gemini 2.5 ProGoogle
Coding
19.1
Overall
16.1
Output $/M
$10.00
1
Sonar Reasoning ProPerplexity
Coding
No data
Overall
11.8
Output $/M
$8.00
0
vs boards won of 1 shared

Decision reading

Only Gemini 2.5 Pro has a coding score, 19.1 on the Vibe Coding Index; Sonar Reasoning Pro has none, so the two cannot be ranked on coding.

Sonar Reasoning Pro has the edge where output tokens cost 1.3x less.

Shared results
1
Gemini 2.5 Pro only
86
Sonar Reasoning Pro only
0
Like-for-like categories
1 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
Gemini 2.5 Pro 19.1 from 3 benchmarks, Sonar Reasoning Pro not benchmarked.
Widest category
Intelligence, 4.3 points apart on average over 1 shared benchmark.
Widest single gap
AA Intelligence Index: Gemini 2.5 Pro 16.1 against Sonar Reasoning Pro 11.8, 4.3 points apart on a 0 to 100 scale.
Output price
Gemini 2.5 Pro $10.00 against Sonar Reasoning Pro $8.00 per million output tokens, 1.3x apart.
Context window
Gemini 2.5 Pro takes 1.05M tokens, Sonar Reasoning Pro 128K.

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 ProBarsSonar Reasoning ProTies
Intelligence10
All shared boards10
CodingDirectional onlySix of these boards feed the Vibe Coding Index15 boards, none shared
Coding benchmarks for Gemini 2.5 Pro and Sonar Reasoning Pro
BenchmarkGemini 2.5 ProSonar Reasoning ProLeader
SWE-bench Verified53.6%Not measuredOnly Gemini 2.5 Pro measured
LMArena WebDev1226Not measuredOnly Gemini 2.5 Pro measured
AA Coding Index33.3Not measuredOnly Gemini 2.5 Pro measured
SWE-bench Verified (Vals AI)54.4%Not measuredOnly Gemini 2.5 Pro measured
Vibe Code Bench (Vals AI)0.4%Not measuredOnly Gemini 2.5 Pro measured
LiveCodeBench (Artificial Analysis run)80.1%Not measuredOnly Gemini 2.5 Pro measured
SciCode46.3%Not measuredOnly Gemini 2.5 Pro measured
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: coding1452Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: software and IT1459Not measuredOnly Gemini 2.5 Pro measured
LMArena WebDev: HTML1235Not measuredOnly Gemini 2.5 Pro measured
LMArena WebDev: from an image1285Not measuredOnly Gemini 2.5 Pro measured
Neither model has a result on the other 23 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for Gemini 2.5 Pro and Sonar Reasoning Pro
BenchmarkGemini 2.5 ProSonar Reasoning ProLeader
AA Intelligence Index16.111.8Gemini 2.5 Pro
Epoch Capabilities Index145.1Not measuredOnly Gemini 2.5 Pro measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
Agentic work benchmarks for Gemini 2.5 Pro and Sonar Reasoning Pro
BenchmarkGemini 2.5 ProSonar Reasoning ProLeader
AA Agentic Index1.6Not measuredOnly Gemini 2.5 Pro measured
τ²-Bench Telecom54.1%Not measuredOnly Gemini 2.5 Pro 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
Neither model has a result on the other 34 boards here
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
Reasoning benchmarks for Gemini 2.5 Pro and Sonar Reasoning Pro
BenchmarkGemini 2.5 ProSonar Reasoning ProLeader
GPQA Diamond (Artificial Analysis run)84.4%Not measuredOnly Gemini 2.5 Pro measured
Humanity's Last Exam (Artificial Analysis run)22.5%Not measuredOnly Gemini 2.5 Pro measured
GPQA Diamond (Epoch AI run)85.3%Not measuredOnly Gemini 2.5 Pro measured
ARC-AGI41.0%Not measuredOnly Gemini 2.5 Pro measured
ARC-AGI-24.9%Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: hard prompts1455Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: expert1452Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: hard prompts in English1453Not measuredOnly Gemini 2.5 Pro measured
LMArena Vision: diagrams1274Not measuredOnly Gemini 2.5 Pro measured
LMArena Vision: homework1281Not measuredOnly Gemini 2.5 Pro measured
Neither model has a result on the other 11 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Math benchmarks for Gemini 2.5 Pro and Sonar Reasoning Pro
BenchmarkGemini 2.5 ProSonar Reasoning ProLeader
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
LMArena Text: math1450Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: mathematical work1454Not measuredOnly Gemini 2.5 Pro measured
Neither model has a result on the other 6 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
Knowledge benchmarks for Gemini 2.5 Pro and Sonar Reasoning Pro
BenchmarkGemini 2.5 ProSonar Reasoning ProLeader
CaseLaw (Vals AI)63.9%Not measuredOnly Gemini 2.5 Pro measured
CorpFin (Vals AI)60.8%Not measuredOnly Gemini 2.5 Pro measured
MortgageTax (Vals AI)68.9%Not measuredOnly Gemini 2.5 Pro measured
MedCode (Vals AI)50.6%Not measuredOnly Gemini 2.5 Pro measured
MMLU Pro (Artificial Analysis run)86.2%Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: science1480Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: medicine and healthcare1476Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: legal and government1480Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: business and finance1439Not measuredOnly Gemini 2.5 Pro measured
Neither model has a result on the other 8 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
Web design benchmarks for Gemini 2.5 Pro and Sonar Reasoning Pro
BenchmarkGemini 2.5 ProSonar Reasoning ProLeader
Design Arena: websites1167Not measuredOnly Gemini 2.5 Pro measured
Design Arena: UI components1140Not measuredOnly Gemini 2.5 Pro measured
Design Arena: game dev1111Not measuredOnly Gemini 2.5 Pro measured
Design Arena: data viz1221Not 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
Neither model has a result on the other 3 boards here
GeneralDirectional onlyPublic boards, shown as published, kept out of the index33 boards, none shared
General benchmarks for Gemini 2.5 Pro and Sonar Reasoning Pro
BenchmarkGemini 2.5 ProSonar Reasoning ProLeader
SAGE (Vals AI)41.9%Not measuredOnly Gemini 2.5 Pro measured
MedScribe (Vals AI)73.5%Not measuredOnly Gemini 2.5 Pro measured
IFBench48.7%Not measuredOnly Gemini 2.5 Pro measured
Long context reasoning (AA-LCR)69.0%Not measuredOnly Gemini 2.5 Pro measured
DTBench82.4%Not measuredOnly Gemini 2.5 Pro measured
LMCA34.8%Not measuredOnly Gemini 2.5 Pro measured
Creative writing (Lech Mazur)83.8%Not measuredOnly Gemini 2.5 Pro measured
LMArena Text1458Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: instruction following1437Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: creative writing1454Not measuredOnly Gemini 2.5 Pro measured
LMArena Vision1263Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: English1458Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: non-English1452Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: Chinese1507Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: German1487Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: Japanese1461Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: Korean1434Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: French1473Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: Spanish1474Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: Russian1461Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: Polish1479Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: multi-turn1453Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: longer queries1449Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: writing and language1454Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: entertainment and media1441Not measuredOnly Gemini 2.5 Pro measured
LMArena Vision: English1261Not measuredOnly Gemini 2.5 Pro measured
LMArena Vision: Chinese1300Not measuredOnly Gemini 2.5 Pro measured
LMArena Vision: reading text1270Not measuredOnly Gemini 2.5 Pro measured
LMArena Vision: humor1262Not measuredOnly Gemini 2.5 Pro measured
LMArena Vision: captioning1304Not measuredOnly Gemini 2.5 Pro measured
LMArena Vision: recognizing things1283Not measuredOnly Gemini 2.5 Pro measured
LMArena Vision: creative writing1265Not measuredOnly Gemini 2.5 Pro measured
LMArena Document1421Not measuredOnly Gemini 2.5 Pro 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 Gemini 2.5 Pro and Sonar Reasoning Pro
BenchmarkGemini 2.5 ProSonar Reasoning ProLeader
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
Sonar Reasoning Pro$0.0060Fits in one request

Sonar Reasoning Pro costs less

Repository review

50K input + 3K output tokens

Gemini 2.5 Pro$0.092Fits in one request
Sonar Reasoning Pro$0.124Fits in one request

Gemini 2.5 Pro costs less

Agent loop

200K input + 10K output tokens

Gemini 2.5 Pro$0.350Fits in one request
Sonar Reasoning Pro$0.480Exceeds the context window

Gemini 2.5 Pro 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 Sonar Reasoning Pro
SpecificationGoogle Gemini 2.5 ProPerplexity Sonar Reasoning Pro
MakerGooglePerplexity
WeightsNo public weights listedNo public weights listed
ReleasedJune 17, 2025March 7, 2025
Context window1.05M128K
Input $/Mtok$1.25$2.00
Output $/Mtok$10.00$8.00
Coding index19.1Not benchmarked
Independent measurements behind it30
Cost per solved task$0.64Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Gemini 2.5 Pro better than Sonar Reasoning Pro for coding?

Only Gemini 2.5 Pro has a coding score, 19.1 on the Vibe Coding Index; Sonar Reasoning Pro has none, so the two cannot be ranked on coding. Sonar Reasoning Pro has the edge where output tokens cost 1.3x less.

Which is cheaper, Gemini 2.5 Pro or Sonar Reasoning Pro?

Gemini 2.5 Pro costs $1.25 per million input tokens and $10.00 per million output tokens; Sonar Reasoning Pro costs $2.00 and $8.00. Cost per solved task is known for Gemini 2.5 Pro only.

Which has the bigger context window, Gemini 2.5 Pro or Sonar Reasoning Pro?

Gemini 2.5 Pro, with 1.05M tokens against 128K.

How many benchmarks is the Gemini 2.5 Pro vs Sonar Reasoning Pro comparison based on?

1 benchmarks have a result for both models as of October 21, 2026. Gemini 2.5 Pro's coding index rests on 3 independent benchmarks and Sonar Reasoning Pro's on 0. Where only one model has a result, the page says so instead of naming a winner.

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