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Gemini 2.5 Flash vs GLM 4.7

Updated October 21, 2026. 52 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace Gemini 2.5 Flash Replace GLM 4.7
Gemini 2.5 FlashGoogle
Coding
28.7
Overall
15.5
Output $/M
$2.50
5
GLM 4.7Z.ai
Coding
33.6
Overall
22.2
Output $/M
$1.98
47
vs boards won of 52 shared

Decision reading

Pick GLM 4.7 for coding: it scores 33.6 on the Vibe Coding Index against 28.7 for Gemini 2.5 Flash.

Pick Gemini 2.5 Flash when the context window is larger, 1.05M against 205K tokens, or when what matters is LMArena Text: expert or LMArena Text: mathematical work, where it leads.

Model AGemini 2.5 FlashGoogle28.7/1001 independent benchmark$0.466 per solved taskModel BGLM 4.7Z.ai33.6/1001 independent benchmark$0.29 per solved task
Shared results
52
Gemini 2.5 Flash only
17
GLM 4.7 only
28
Like-for-like categories
8 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 Flash 28.7 from 1 benchmark, GLM 4.7 33.6 from 1 benchmark.
Widest category
Agentic work, 37.9 points apart on average over 2 shared benchmarks.
Widest single gap
τ²-Bench Telecom: Gemini 2.5 Flash 31.6% against GLM 4.7 95.9%, 64.3 points apart on a 0 to 100 scale.
Output price
Gemini 2.5 Flash $2.50 against GLM 4.7 $1.98 per million output tokens, 1.3x apart.
Context window
Gemini 2.5 Flash takes 1.05M tokens, GLM 4.7 205K.

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 FlashBarsGLM 4.7Ties
Coding05
Intelligence02
Agentic work02
Reasoning15
Math13
Knowledge15
Web design07
General218
All shared boards547
CodingLike-for-likeFour of these boards feed the Vibe Coding Index05
Coding benchmarks for Gemini 2.5 Flash and GLM 4.7
BenchmarkGemini 2.5 FlashGLM 4.7Leader
SWE-bench Verified28.7%Not measuredOnly Gemini 2.5 Flash measured
LMArena WebDevNot measured1435Only GLM 4.7 measured
AA Coding IndexNot measured45.3Only GLM 4.7 measured
LiveCodeBench (Vals AI)Not measured82.2%Only GLM 4.7 measured
SWE-bench Verified (Vals AI)Not measured69.4%Only GLM 4.7 measured
LiveCodeBench (Artificial Analysis run)69.5%89.4%GLM 4.7
Terminal-Bench Hard (Artificial Analysis run)13.6%31.8%GLM 4.7
Terminal-Bench17.1%33.4%GLM 4.7
ALE-BenchNot measured399.5Only GLM 4.7 measured
LMArena Text: coding14231454GLM 4.7
LMArena Text: software and IT14251453GLM 4.7
LMArena WebDev: ReactNot measured1420Only GLM 4.7 measured
LMArena WebDev: HTMLNot measured1447Only GLM 4.7 measured
Neither model has a result on the other 25 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for Gemini 2.5 Flash and GLM 4.7
BenchmarkGemini 2.5 FlashGLM 4.7Leader
AA Intelligence Index15.522.2GLM 4.7
Epoch Capabilities Index142.9143.6GLM 4.7
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
Agentic work benchmarks for Gemini 2.5 Flash and GLM 4.7
BenchmarkGemini 2.5 FlashGLM 4.7Leader
Design Arena: agents building full-stack appsNot measured1050Only GLM 4.7 measured
Design Arena: agents building mobile appsNot measured1105Only GLM 4.7 measured
Design Arena: agents building Android appsNot measured1118Only GLM 4.7 measured
Design Arena: agents building Godot gamesNot measured1058Only GLM 4.7 measured
τ²-Bench Telecom31.6%95.9%GLM 4.7
BALROG33.5%Not measuredOnly Gemini 2.5 Flash measured
Vending-Bench 2$549$2,377GLM 4.7
Neither model has a result on the other 32 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index15
Reasoning benchmarks for Gemini 2.5 Flash and GLM 4.7
BenchmarkGemini 2.5 FlashGLM 4.7Leader
GPQA DiamondNot measured80.0%Only GLM 4.7 measured
GPQA Diamond (Artificial Analysis run)79.0%85.9%GLM 4.7
Humanity's Last Exam (Artificial Analysis run)12.1%27.4%GLM 4.7
GPQA Diamond (Epoch AI run)Not measured83.3%Only GLM 4.7 measured
ARC-AGI33.3%Not measuredOnly Gemini 2.5 Flash measured
ARC-AGI-22.5%Not measuredOnly Gemini 2.5 Flash measured
SimpleBench41.2%47.7%GLM 4.7
Chess puzzlesNot measured21.0%Only GLM 4.7 measured
LMArena Text: hard prompts14221443GLM 4.7
LMArena Text: expert14261424Gemini 2.5 Flash
LMArena Text: hard prompts in English14201452GLM 4.7
LMArena Vision: diagrams1245Not measuredOnly Gemini 2.5 Flash measured
LMArena Vision: homework1244Not measuredOnly Gemini 2.5 Flash measured
Neither model has a result on the other 8 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index13
Math benchmarks for Gemini 2.5 Flash and GLM 4.7
BenchmarkGemini 2.5 FlashGLM 4.7Leader
AIMENot measured93.3%Only GLM 4.7 measured
MGSM (Vals AI)Not measured88.2%Only GLM 4.7 measured
AA Math Index73.395.0GLM 4.7
AIME 2025 (Artificial Analysis run)73.3%95.0%GLM 4.7
OTIS Mock AIMENot measured83.3%Only GLM 4.7 measured
ProofBenchNot measured6.0%Only GLM 4.7 measured
LMArena Text: math14111424GLM 4.7
LMArena Text: mathematical work14221411Gemini 2.5 Flash
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index15
Knowledge benchmarks for Gemini 2.5 Flash and GLM 4.7
BenchmarkGemini 2.5 FlashGLM 4.7Leader
MMLU ProNot measured82.7%Only GLM 4.7 measured
LegalBench (Vals AI)Not measured83.4%Only GLM 4.7 measured
CaseLaw (Vals AI)Not measured54.9%Only GLM 4.7 measured
CorpFin (Vals AI)Not measured46.4%Only GLM 4.7 measured
TaxEval (Vals AI)Not measured68.8%Only GLM 4.7 measured
MedQA (Vals AI)Not measured93.7%Only GLM 4.7 measured
MedCode (Vals AI)40.4%32.8%Gemini 2.5 Flash
MMLU Pro (Artificial Analysis run)83.2%85.6%GLM 4.7
SimpleQA VerifiedNot measured32.2%Only GLM 4.7 measured
LMArena Text: science14331457GLM 4.7
LMArena Text: medicine and healthcare14281469GLM 4.7
LMArena Text: legal and government14301444GLM 4.7
LMArena Text: business and finance14041429GLM 4.7
Neither model has a result on the other 4 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index07
Web design benchmarks for Gemini 2.5 Flash and GLM 4.7
BenchmarkGemini 2.5 FlashGLM 4.7Leader
Design Arena: websites10741226GLM 4.7
Design Arena: UI components10051199GLM 4.7
Design Arena: game dev10051189GLM 4.7
Design Arena: data viz10601195GLM 4.7
Design Arena: 3D10211191GLM 4.7
Design Arena: all code categories10591217GLM 4.7
Design Arena: SVG10181142GLM 4.7
Design Arena: ASCII artNot measured1165Only GLM 4.7 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index218
General benchmarks for Gemini 2.5 Flash and GLM 4.7
BenchmarkGemini 2.5 FlashGLM 4.7Leader
SAGE (Vals AI)44.8%Not measuredOnly Gemini 2.5 Flash measured
MedScribe (Vals AI)83.0%68.6%Gemini 2.5 Flash
IFBench50.3%67.9%GLM 4.7
Long context reasoning (AA-LCR)65.3%71.0%GLM 4.7
DTBench76.5%Not measuredOnly Gemini 2.5 Flash measured
LMCA27.5%Not measuredOnly Gemini 2.5 Flash measured
CL-benchNot measured15.9%Only GLM 4.7 measured
CL-bench LifeNot measured10.9%Only GLM 4.7 measured
LMArena Text14171435GLM 4.7
LMArena Text: instruction following14051412GLM 4.7
LMArena Text: creative writing14001401GLM 4.7
LMArena Vision1235Not measuredOnly Gemini 2.5 Flash measured
LMArena Text: English14181453GLM 4.7
LMArena Text: non-English14091417GLM 4.7
LMArena Text: Chinese14491495GLM 4.7
LMArena Text: German14171426GLM 4.7
LMArena Text: Japanese13961439GLM 4.7
LMArena Text: Korean13851399GLM 4.7
LMArena Text: French14321432Gemini 2.5 Flash
LMArena Text: Spanish14221434GLM 4.7
LMArena Text: Russian14151423GLM 4.7
LMArena Text: Polish14131435GLM 4.7
LMArena Text: multi-turn14081446GLM 4.7
LMArena Text: longer queries14191432GLM 4.7
LMArena Text: writing and language14051411GLM 4.7
LMArena Text: entertainment and media13931400GLM 4.7
LMArena Vision: English1239Not measuredOnly Gemini 2.5 Flash measured
LMArena Vision: Chinese1284Not measuredOnly Gemini 2.5 Flash measured
LMArena Vision: reading text1237Not measuredOnly Gemini 2.5 Flash measured
LMArena Vision: humor1225Not measuredOnly Gemini 2.5 Flash measured
LMArena Vision: captioning1277Not measuredOnly Gemini 2.5 Flash measured
LMArena Vision: recognizing things1252Not measuredOnly Gemini 2.5 Flash measured
LMArena Vision: creative writing1241Not measuredOnly Gemini 2.5 Flash 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 Flash and GLM 4.7
BenchmarkGemini 2.5 FlashGLM 4.7Leader
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 Flash$0.0015Fits in one request
GLM 4.7$0.0015Fits in one request

GLM 4.7 costs less

Repository review

50K input + 3K output tokens

Gemini 2.5 Flash$0.022Fits in one request
GLM 4.7$0.033Fits in one request

Gemini 2.5 Flash costs less

Agent loop

200K input + 10K output tokens

Gemini 2.5 Flash$0.085Fits in one request
GLM 4.7$0.128Exceeds the context window

Gemini 2.5 Flash 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 Flash and GLM 4.7
SpecificationGoogle Gemini 2.5 FlashZ.ai GLM 4.7
MakerGoogleZ.ai
WeightsNo public weights listedOpen weights
ReleasedJune 17, 2025December 22, 2025
Context window1.05M205K
Input $/Mtok$0.30$0.54
Output $/Mtok$2.50$1.98
Coding index28.733.6
Independent measurements behind it11
Cost per solved task$0.466$0.29
SourceOpenRouter listingOpenRouter listing

Questions

Is Gemini 2.5 Flash better than GLM 4.7 for coding?

Pick GLM 4.7 for coding: it scores 33.6 on the Vibe Coding Index against 28.7 for Gemini 2.5 Flash. Pick Gemini 2.5 Flash when the context window is larger, 1.05M against 205K tokens, or when what matters is LMArena Text: expert or LMArena Text: mathematical work, where it leads.

Which is cheaper, Gemini 2.5 Flash or GLM 4.7?

Gemini 2.5 Flash costs $0.30 per million input tokens and $2.50 per million output tokens; GLM 4.7 costs $0.54 and $1.98. Per solved SWE-bench Verified task, Gemini 2.5 Flash costs $0.466 and GLM 4.7 $0.29.

Which has the bigger context window, Gemini 2.5 Flash or GLM 4.7?

Gemini 2.5 Flash, with 1.05M tokens against 205K.

How many benchmarks is the Gemini 2.5 Flash vs GLM 4.7 comparison based on?

52 benchmarks have a result for both models as of October 21, 2026. Gemini 2.5 Flash's coding index rests on 1 independent benchmark and GLM 4.7's on 1. Where only one model has a result, the page says so instead of naming a winner.

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