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Gemini 2.5 Pro Preview 06-05 vs GLM 5V Turbo

GLM 5V Turbo costs 2.5x less per output token than Gemini 2.5 Pro Preview 06-05 ($4.00 vs $10.00 per million). Updated October 22, 2026; 2 benchmarks have a result for both.

Gemini 2.5 Pro Preview 06-05Google
Coding
No data
Overall
No data
Output $/M
$10.00
2
GLM 5V TurboZ.ai
Coding
No data
Overall
No data
Output $/M
$4.00
0
vs boards won of 2 shared

Gemini 2.5 Pro Preview 06-05 or GLM 5V Turbo: which to pick

Neither model has a coding score yet, so the two cannot be ranked on coding.

Pick Gemini 2.5 Pro Preview 06-05 for a larger context window (1.05M against 203K tokens).

Shared results
2
Gemini 2.5 Pro Preview 06-05 only
13
GLM 5V Turbo only
63
Like-for-like categories
1 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 Preview 06-05 not benchmarked, GLM 5V Turbo not benchmarked.
Widest category
Reasoning, 2.7 points apart on average over 2 shared benchmarks.
Widest single gap
Humanity's Last Exam (Artificial Analysis run): Gemini 2.5 Pro Preview 06-05 19.9% against GLM 5V Turbo 17.1%, 2.8 points apart on a 0 to 100 scale.
Output price
Gemini 2.5 Pro Preview 06-05 $10.00 against GLM 5V Turbo $4.00 per million output tokens, 2.5x apart.
Context window
Gemini 2.5 Pro Preview 06-05 takes 1.05M tokens, GLM 5V Turbo 203K.

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 Pro Preview 06-05BarsGLM 5V TurboTies
Reasoning20
All shared boards20
CodingDirectional onlySix of these boards feed the Vibe Coding Index12 boards, none shared
Coding benchmarks for Gemini 2.5 Pro Preview 06-05 and GLM 5V Turbo
BenchmarkGemini 2.5 Pro Preview 06-05GLM 5V TurboLeader
Aider polyglot83.1%Not measuredOnly Gemini 2.5 Pro Preview 06-05 measured
LMArena WebDevNot measured1402Only GLM 5V Turbo measured
LiveCodeBench (Vals AI)79.2%Not measuredOnly Gemini 2.5 Pro Preview 06-05 measured
LiveCodeBench (Artificial Analysis run)77.8%Not measuredOnly Gemini 2.5 Pro Preview 06-05 measured
Terminal-Bench Hard (Artificial Analysis run)Not measured32.6%Only GLM 5V Turbo measured
GSO-Bench3.9%Not measuredOnly Gemini 2.5 Pro Preview 06-05 measured
LMArena Text: codingNot measured1466Only GLM 5V Turbo measured
LMArena Text: software and ITNot measured1460Only GLM 5V Turbo measured
LMArena WebDev: ReactNot measured1386Only GLM 5V Turbo measured
LMArena WebDev: HTMLNot measured1356Only GLM 5V Turbo measured
LMArena WebDev: from an imageNot measured1425Only GLM 5V Turbo measured
SciCode7.7%Not measuredOnly Gemini 2.5 Pro Preview 06-05 measured
Neither model has a result on the other 27 boards here
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
Intelligence benchmarks for Gemini 2.5 Pro Preview 06-05 and GLM 5V Turbo
BenchmarkGemini 2.5 Pro Preview 06-05GLM 5V TurboLeader
AA Intelligence IndexNot measured23.5Only GLM 5V Turbo measured
Neither model has a result on the other 1 board here
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
Agentic work benchmarks for Gemini 2.5 Pro Preview 06-05 and GLM 5V Turbo
BenchmarkGemini 2.5 Pro Preview 06-05GLM 5V TurboLeader
Design Arena: agents building web appsNot measured1124Only GLM 5V Turbo measured
Design Arena: agents building full-stack appsNot measured1149Only GLM 5V Turbo measured
Design Arena: agents building mobile appsNot measured1134Only GLM 5V Turbo measured
Design Arena: agents building Android appsNot measured1267Only GLM 5V Turbo measured
Design Arena: agents building Godot gamesNot measured957Only GLM 5V Turbo measured
Design Arena: agents building gamesNot measured1102Only GLM 5V Turbo measured
Design Arena: HTML slidesNot measured1070Only GLM 5V Turbo measured
Design Arena: PowerPoint slidesNot measured1127Only GLM 5V Turbo measured
τ²-Bench TelecomNot measured98.5%Only GLM 5V Turbo measured
METR time horizons55.4%Not measuredOnly Gemini 2.5 Pro Preview 06-05 measured
Remote Labor Index0.8%Not measuredOnly Gemini 2.5 Pro Preview 06-05 measured
DeepResearch Bench42.8%Not measuredOnly Gemini 2.5 Pro Preview 06-05 measured
Neither model has a result on the other 29 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
Reasoning benchmarks for Gemini 2.5 Pro Preview 06-05 and GLM 5V Turbo
BenchmarkGemini 2.5 Pro Preview 06-05GLM 5V TurboLeader
GPQA Diamond (Artificial Analysis run)83.6%80.9%Gemini 2.5 Pro Preview 06-05
Humanity's Last Exam (Artificial Analysis run)19.9%17.1%Gemini 2.5 Pro Preview 06-05
GPQA Diamond (Epoch AI run)84.8%Not measuredOnly Gemini 2.5 Pro Preview 06-05 measured
SimpleBench62.4%Not measuredOnly Gemini 2.5 Pro Preview 06-05 measured
VPCT46.4%Not measuredOnly Gemini 2.5 Pro Preview 06-05 measured
LMArena Text: hard promptsNot measured1443Only GLM 5V Turbo measured
LMArena Text: expertNot measured1453Only GLM 5V Turbo measured
LMArena Text: hard prompts in EnglishNot measured1456Only GLM 5V Turbo measured
LMArena Vision: diagramsNot measured1286Only GLM 5V Turbo measured
LMArena Vision: homeworkNot measured1273Only GLM 5V Turbo measured
Neither model has a result on the other 12 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Math benchmarks for Gemini 2.5 Pro Preview 06-05 and GLM 5V Turbo
BenchmarkGemini 2.5 Pro Preview 06-05GLM 5V TurboLeader
LMArena Text: mathNot measured1442Only GLM 5V Turbo measured
LMArena Text: mathematical workNot measured1447Only GLM 5V Turbo measured
Neither model has a result on the other 11 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
Knowledge benchmarks for Gemini 2.5 Pro Preview 06-05 and GLM 5V Turbo
BenchmarkGemini 2.5 Pro Preview 06-05GLM 5V TurboLeader
MMLU Pro (Artificial Analysis run)85.8%Not measuredOnly Gemini 2.5 Pro Preview 06-05 measured
LMArena Text: scienceNot measured1448Only GLM 5V Turbo measured
LMArena Text: medicine and healthcareNot measured1433Only GLM 5V Turbo measured
LMArena Text: legal and governmentNot measured1436Only GLM 5V Turbo measured
LMArena Text: business and financeNot measured1435Only GLM 5V Turbo measured
Neither model has a result on the other 13 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for Gemini 2.5 Pro Preview 06-05 and GLM 5V Turbo
BenchmarkGemini 2.5 Pro Preview 06-05GLM 5V TurboLeader
Design Arena: websitesNot measured1230Only GLM 5V Turbo measured
Design Arena: UI componentsNot measured1210Only GLM 5V Turbo measured
Design Arena: game devNot measured1218Only GLM 5V Turbo measured
Design Arena: data vizNot measured1202Only GLM 5V Turbo measured
Design Arena: 3DNot measured1208Only GLM 5V Turbo measured
Design Arena: all code categoriesNot measured1228Only GLM 5V Turbo measured
Design Arena: SVGNot measured1151Only GLM 5V Turbo measured
Design Arena: ASCII artNot measured1117Only GLM 5V Turbo measured
Neither model has a result on the other 1 board here
GeneralDirectional onlyPublic boards, shown as published, kept out of the index28 boards, none shared
General benchmarks for Gemini 2.5 Pro Preview 06-05 and GLM 5V Turbo
BenchmarkGemini 2.5 Pro Preview 06-05GLM 5V TurboLeader
IFBenchNot measured61.1%Only GLM 5V Turbo measured
Long context reasoning (AA-LCR)Not measured70.3%Only GLM 5V Turbo measured
Fiction.LiveBench91.7%Not measuredOnly Gemini 2.5 Pro Preview 06-05 measured
LMArena TextNot measured1437Only GLM 5V Turbo measured
LMArena Text: instruction followingNot measured1423Only GLM 5V Turbo measured
LMArena Text: creative writingNot measured1417Only GLM 5V Turbo measured
LMArena VisionNot measured1264Only GLM 5V Turbo measured
LMArena Text: EnglishNot measured1454Only GLM 5V Turbo measured
LMArena Text: non-EnglishNot measured1420Only GLM 5V Turbo measured
LMArena Text: ChineseNot measured1491Only GLM 5V Turbo measured
LMArena Text: GermanNot measured1422Only GLM 5V Turbo measured
LMArena Text: KoreanNot measured1397Only GLM 5V Turbo measured
LMArena Text: FrenchNot measured1443Only GLM 5V Turbo measured
LMArena Text: SpanishNot measured1452Only GLM 5V Turbo measured
LMArena Text: RussianNot measured1432Only GLM 5V Turbo measured
LMArena Text: PolishNot measured1432Only GLM 5V Turbo measured
LMArena Text: multi-turnNot measured1431Only GLM 5V Turbo measured
LMArena Text: longer queriesNot measured1438Only GLM 5V Turbo measured
LMArena Text: writing and languageNot measured1420Only GLM 5V Turbo measured
LMArena Text: entertainment and mediaNot measured1407Only GLM 5V Turbo measured
LMArena Vision: EnglishNot measured1274Only GLM 5V Turbo measured
LMArena Vision: ChineseNot measured1329Only GLM 5V Turbo measured
LMArena Vision: reading textNot measured1270Only GLM 5V Turbo measured
LMArena Vision: humorNot measured1268Only GLM 5V Turbo measured
LMArena Vision: captioningNot measured1264Only GLM 5V Turbo measured
LMArena Vision: recognizing thingsNot measured1241Only GLM 5V Turbo measured
LMArena Vision: creative writingNot measured1252Only GLM 5V Turbo measured
LMArena DocumentNot measured1416Only GLM 5V Turbo measured
Neither model has a result on the other 18 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 Preview 06-05 and GLM 5V Turbo
BenchmarkGemini 2.5 Pro Preview 06-05GLM 5V TurboLeader
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 Preview 06-05$0.0063Fits in one request
GLM 5V Turbo$0.0032Fits in one request

GLM 5V Turbo costs less

Repository review

50K input + 3K output tokens

Gemini 2.5 Pro Preview 06-05$0.092Fits in one request
GLM 5V Turbo$0.072Fits in one request

GLM 5V Turbo costs less

Agent loop

200K input + 10K output tokens

Gemini 2.5 Pro Preview 06-05$0.350Fits in one request
GLM 5V Turbo$0.280Exceeds the context window

GLM 5V Turbo 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 Preview 06-05 and GLM 5V Turbo
SpecificationGoogle Gemini 2.5 Pro Preview 06-05Z.ai GLM 5V Turbo
MakerGoogleZ.ai
WeightsNo public weights listedNo public weights listed
ReleasedJune 5, 2025April 1, 2026
Context window1.05M203K
Input $/Mtok$1.25$1.20
Output $/Mtok$10.00$4.00
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it01
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Gemini 2.5 Pro Preview 06-05 better than GLM 5V Turbo for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Gemini 2.5 Pro Preview 06-05 for a larger context window (1.05M against 203K tokens).

Which is cheaper, Gemini 2.5 Pro Preview 06-05 or GLM 5V Turbo?

Gemini 2.5 Pro Preview 06-05 costs $1.25 per million input tokens and $10.00 per million output tokens; GLM 5V Turbo costs $1.20 and $4.00. Cost per solved task is known for neither of them.

Which has the bigger context window, Gemini 2.5 Pro Preview 06-05 or GLM 5V Turbo?

Gemini 2.5 Pro Preview 06-05, with 1.05M tokens against 203K.

How many benchmarks is the Gemini 2.5 Pro Preview 06-05 vs GLM 5V Turbo comparison based on?

2 benchmarks have a result for both models as of October 22, 2026. Gemini 2.5 Pro Preview 06-05's coding index rests on 0 independent benchmarks and GLM 5V Turbo's on 1. Where only one model has a result, the page says so instead of naming a winner.

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