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Gemini 3.5 Flash vs GLM 4.7

GLM 4.7 leads Gemini 3.5 Flash on coding, 55.1 to 39.8 on the Vibe Coding Index, and GLM 4.7 costs 4.1x less per output token ($2.20 vs $9.00 per million). Updated October 22, 2026; 68 benchmarks have a result for both.

Replace Gemini 3.5 Flash Replace GLM 4.7
Gemini 3.5 FlashGoogle
Coding
39.8
Overall
63.8
Output $/M
$9.00
67
GLM 4.7Z.ai
Coding
55.1
Overall
56.9
Output $/M
$2.20
1
vs boards won of 68 shared

Gemini 3.5 Flash or GLM 4.7: which to pick

Pick GLM 4.7 for coding: it scores 55.1 on the Vibe Coding Index against 39.8 for Gemini 3.5 Flash, though on 2 benchmarks against 4.

Pick Gemini 3.5 Flash for a larger context window (1.05M against 205K tokens) and work measured by LMArena WebDev or LiveCodeBench (Vals AI), where it leads.

Model AGemini 3.5 FlashGoogle39.8/1004 independent benchmarks$1.21 per solved taskModel BGLM 4.7Z.ai55.1/1002 independent benchmarks$0.29 per solved task
Shared results
68
Gemini 3.5 Flash only
44
GLM 4.7 only
13
Like-for-like categories
8 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 3.5 Flash 39.8 from 4 benchmarks, GLM 4.7 55.1 from 2 benchmarks.
Widest category
Math, 16.1 points apart on average over 4 shared benchmarks.
Widest single gap
SimpleQA Verified: Gemini 3.5 Flash 66.2% against GLM 4.7 32.2%, 34.0 points apart on a 0 to 100 scale.
Output price
Gemini 3.5 Flash $9.00 against GLM 4.7 $2.20 per million output tokens, 4.1x apart.
Context window
Gemini 3.5 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 3.5 FlashBarsGLM 4.7Ties
Coding90
Intelligence20
Agentic work51
Reasoning90
Math40
Knowledge100
Web design80
General200
All shared boards671
CodingLike-for-likeSix of these boards feed the Vibe Coding Index90
Coding benchmarks for Gemini 3.5 Flash and GLM 4.7
BenchmarkGemini 3.5 FlashGLM 4.7Leader
LMArena WebDev14991435Gemini 3.5 Flash
AA Coding IndexNot measured45.3Only GLM 4.7 measured
LiveCodeBench (Vals AI)87.6%82.2%Gemini 3.5 Flash
SWE-bench Verified (Vals AI)78.8%69.4%Gemini 3.5 Flash
Terminal-Bench 4 (Vals AI)6.1%Not measuredOnly Gemini 3.5 Flash measured
Vibe Code Bench (Vals AI)48.7%Not measuredOnly Gemini 3.5 Flash measured
Code migration (Vals AI)26.8%Not measuredOnly Gemini 3.5 Flash measured
ProgramBench (Vals AI)0.0%Not measuredOnly Gemini 3.5 Flash measured
Terminal-Bench 2.1 (Vals AI)74.2%Not measuredOnly Gemini 3.5 Flash measured
SkillsBench (Vals AI)52.7%Not measuredOnly Gemini 3.5 Flash measured
LiveCodeBench (Artificial Analysis run)Not measured89.4%Only GLM 4.7 measured
SciCode53.9%Not measuredOnly Gemini 3.5 Flash measured
Terminal-Bench Hard (Artificial Analysis run)46.2%31.8%Gemini 3.5 Flash
Terminal-BenchNot measured33.4%Only GLM 4.7 measured
WeirdML62.6%Not measuredOnly Gemini 3.5 Flash measured
ALE-Bench911.0399.5Gemini 3.5 Flash
LMArena Text: coding14931454Gemini 3.5 Flash
LMArena Text: software and IT14941453Gemini 3.5 Flash
LMArena WebDev: React14951420Gemini 3.5 Flash
LMArena WebDev: HTML15221447Gemini 3.5 Flash
LMArena WebDev: from an image1504Not measuredOnly Gemini 3.5 Flash measured
SciCode12.3%Not measuredOnly Gemini 3.5 Flash measured
Neither model has a result on the other 17 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for Gemini 3.5 Flash and GLM 4.7
BenchmarkGemini 3.5 FlashGLM 4.7Leader
AA Intelligence Index33.622.2Gemini 3.5 Flash
Epoch Capabilities Index154.9143.6Gemini 3.5 Flash
Agentic workLike-for-likePublic boards, shown as published, kept out of the index51
Agentic work benchmarks for Gemini 3.5 Flash and GLM 4.7
BenchmarkGemini 3.5 FlashGLM 4.7Leader
Design Arena: agents building web apps1202Not measuredOnly Gemini 3.5 Flash measured
Design Arena: agents building full-stack apps11971050Gemini 3.5 Flash
Design Arena: agents building mobile apps11741105Gemini 3.5 Flash
Design Arena: agents building Android apps11851112Gemini 3.5 Flash
Design Arena: agents building Godot games10981058Gemini 3.5 Flash
Design Arena: agents building games1180Not measuredOnly Gemini 3.5 Flash measured
Design Arena: HTML slides1093Not measuredOnly Gemini 3.5 Flash measured
Design Arena: PowerPoint slides1154Not measuredOnly Gemini 3.5 Flash measured
Finance Agent (Vals AI)57.9%Not measuredOnly Gemini 3.5 Flash measured
Tax Agent Bench (Vals AI)21.6%Not measuredOnly Gemini 3.5 Flash measured
Legal Research Bench (Vals AI)30.8%Not measuredOnly Gemini 3.5 Flash measured
Excel Modeling Benchmark (Vals AI)63.5%Not measuredOnly Gemini 3.5 Flash measured
Terminal-Bench-Science (Vals AI)5.7%Not measuredOnly Gemini 3.5 Flash measured
τ²-Bench Telecom95.6%95.9%GLM 4.7
APEX-Agents27.5%Not measuredOnly Gemini 3.5 Flash measured
Vending-Bench 2$5,396$2,377Gemini 3.5 Flash
BrowseComp-PlusNot measured64.7%Only GLM 4.7 measured
Neither model has a result on the other 24 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index90
Reasoning benchmarks for Gemini 3.5 Flash and GLM 4.7
BenchmarkGemini 3.5 FlashGLM 4.7Leader
GPQA Diamond92.7%80.0%Gemini 3.5 Flash
MMMU Pro (Vals AI)88.3%Not measuredOnly Gemini 3.5 Flash measured
GPQA Diamond (Artificial Analysis run)92.2%85.9%Gemini 3.5 Flash
Humanity's Last Exam (Artificial Analysis run)42.7%27.4%Gemini 3.5 Flash
GPQA Diamond (Epoch AI run)92.8%83.3%Gemini 3.5 Flash
ARC-AGI92.5%Not measuredOnly Gemini 3.5 Flash measured
ARC-AGI-272.1%Not measuredOnly Gemini 3.5 Flash measured
SimpleBench76.7%47.7%Gemini 3.5 Flash
Chess puzzles45.0%21.0%Gemini 3.5 Flash
Mystery game puzzles32.0%Not measuredOnly Gemini 3.5 Flash measured
EBR-bench4.8%Not measuredOnly Gemini 3.5 Flash measured
LMArena Text: hard prompts14881443Gemini 3.5 Flash
LMArena Text: expert14951424Gemini 3.5 Flash
LMArena Text: hard prompts in English14831452Gemini 3.5 Flash
LMArena Vision: diagrams1323Not measuredOnly Gemini 3.5 Flash measured
LMArena Vision: homework1330Not measuredOnly Gemini 3.5 Flash measured
Neither model has a result on the other 6 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index40
Math benchmarks for Gemini 3.5 Flash and GLM 4.7
BenchmarkGemini 3.5 FlashGLM 4.7Leader
AIMENot measured93.3%Only GLM 4.7 measured
MGSM (Vals AI)Not measured88.2%Only GLM 4.7 measured
AA Math IndexNot measured95.0Only GLM 4.7 measured
AIME 2025 (Artificial Analysis run)Not measured95.0%Only GLM 4.7 measured
OTIS Mock AIME95.6%83.3%Gemini 3.5 Flash
FrontierMath, tiers 1 to 362.8%Not measuredOnly Gemini 3.5 Flash measured
FrontierMath, tier 426.8%Not measuredOnly Gemini 3.5 Flash measured
ProofBench31.0%6.0%Gemini 3.5 Flash
LMArena Text: math15041424Gemini 3.5 Flash
LMArena Text: mathematical work14951411Gemini 3.5 Flash
Neither model has a result on the other 3 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index100
Knowledge benchmarks for Gemini 3.5 Flash and GLM 4.7
BenchmarkGemini 3.5 FlashGLM 4.7Leader
MMLU Pro89.5%82.7%Gemini 3.5 Flash
LegalBench (Vals AI)83.6%83.4%Gemini 3.5 Flash
CaseLaw (Vals AI)Not measured54.9%Only GLM 4.7 measured
CorpFin (Vals AI)64.7%46.4%Gemini 3.5 Flash
TaxEval (Vals AI)74.4%68.8%Gemini 3.5 Flash
MortgageTax (Vals AI)68.1%Not measuredOnly Gemini 3.5 Flash measured
MedQA (Vals AI)Not measured93.7%Only GLM 4.7 measured
MedCode (Vals AI)55.8%32.8%Gemini 3.5 Flash
MMLU Pro (Artificial Analysis run)Not measured85.6%Only GLM 4.7 measured
SimpleQA Verified66.2%32.2%Gemini 3.5 Flash
LMArena Text: science14951457Gemini 3.5 Flash
LMArena Text: medicine and healthcare14831469Gemini 3.5 Flash
LMArena Text: legal and government14881444Gemini 3.5 Flash
LMArena Text: business and finance14671429Gemini 3.5 Flash
FACTS Benchmark Suite63.1%Not measuredOnly Gemini 3.5 Flash measured
Neither model has a result on the other 3 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index80
Web design benchmarks for Gemini 3.5 Flash and GLM 4.7
BenchmarkGemini 3.5 FlashGLM 4.7Leader
Design Arena: websites12671225Gemini 3.5 Flash
Design Arena: UI components12691196Gemini 3.5 Flash
Design Arena: game dev12631188Gemini 3.5 Flash
Design Arena: data viz12361195Gemini 3.5 Flash
Design Arena: 3D12431190Gemini 3.5 Flash
Design Arena: all code categories12661216Gemini 3.5 Flash
Design Arena: SVG12531142Gemini 3.5 Flash
Design Arena: ASCII art12541165Gemini 3.5 Flash
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index200
General benchmarks for Gemini 3.5 Flash and GLM 4.7
BenchmarkGemini 3.5 FlashGLM 4.7Leader
Public Benefits Bench (Vals AI)59.5%Not measuredOnly Gemini 3.5 Flash measured
SAGE (Vals AI)49.9%Not measuredOnly Gemini 3.5 Flash measured
MedScribe (Vals AI)76.6%68.6%Gemini 3.5 Flash
Vals Multimodal Index62.9%Not measuredOnly Gemini 3.5 Flash measured
IFBench76.3%67.9%Gemini 3.5 Flash
Long context reasoning (AA-LCR)74.3%71.0%Gemini 3.5 Flash
DTBench94.7%Not measuredOnly Gemini 3.5 Flash measured
LMCA47.1%Not measuredOnly Gemini 3.5 Flash measured
CL-benchNot measured15.9%Only GLM 4.7 measured
Surface Evolver Bench58.1%Not measuredOnly Gemini 3.5 Flash measured
CL-bench LifeNot measured10.9%Only GLM 4.7 measured
LMArena Text14821435Gemini 3.5 Flash
LMArena Text: instruction following14671412Gemini 3.5 Flash
LMArena Text: creative writing14701401Gemini 3.5 Flash
LMArena Vision1308Not measuredOnly Gemini 3.5 Flash measured
LMArena Text: English14811453Gemini 3.5 Flash
LMArena Text: non-English14751417Gemini 3.5 Flash
LMArena Text: Chinese15271495Gemini 3.5 Flash
LMArena Text: German14941426Gemini 3.5 Flash
LMArena Text: Japanese14861439Gemini 3.5 Flash
LMArena Text: Korean14481399Gemini 3.5 Flash
LMArena Text: French14901432Gemini 3.5 Flash
LMArena Text: Spanish14801434Gemini 3.5 Flash
LMArena Text: Russian14931423Gemini 3.5 Flash
LMArena Text: Polish15161435Gemini 3.5 Flash
LMArena Text: multi-turn14811446Gemini 3.5 Flash
LMArena Text: longer queries14831432Gemini 3.5 Flash
LMArena Text: writing and language14781411Gemini 3.5 Flash
LMArena Text: entertainment and media14591400Gemini 3.5 Flash
LMArena Vision: English1305Not measuredOnly Gemini 3.5 Flash measured
LMArena Vision: Chinese1377Not measuredOnly Gemini 3.5 Flash measured
LMArena Vision: reading text1317Not measuredOnly Gemini 3.5 Flash measured
LMArena Vision: humor1321Not measuredOnly Gemini 3.5 Flash measured
LMArena Vision: creative writing1301Not measuredOnly Gemini 3.5 Flash measured
LMArena Document1463Not measuredOnly Gemini 3.5 Flash 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 3.5 Flash and GLM 4.7
BenchmarkGemini 3.5 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 3.5 Flash$0.0060Fits in one request
GLM 4.7$0.0017Fits in one request

GLM 4.7 costs less

Repository review

50K input + 3K output tokens

Gemini 3.5 Flash$0.102Fits in one request
GLM 4.7$0.037Fits in one request

GLM 4.7 costs less

Agent loop

200K input + 10K output tokens

Gemini 3.5 Flash$0.390Fits in one request
GLM 4.7$0.142Exceeds the context window

GLM 4.7 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 3.5 Flash and GLM 4.7
SpecificationGoogle Gemini 3.5 FlashZ.ai GLM 4.7
MakerGoogleZ.ai
WeightsNo public weights listedOpen weights
ReleasedMay 19, 2026December 22, 2025
Context window1.05M205K
Input $/Mtok$1.50$0.60
Output $/Mtok$9.00$2.20
Coding index39.855.1
Independent measurements behind it42
Cost per solved task$1.21$0.29
SourceOpenRouter listingOpenRouter listing

Questions

Is Gemini 3.5 Flash better than GLM 4.7 for coding?

Pick GLM 4.7 for coding: it scores 55.1 on the Vibe Coding Index against 39.8 for Gemini 3.5 Flash, though on 2 benchmarks against 4. Pick Gemini 3.5 Flash for a larger context window (1.05M against 205K tokens) and work measured by LMArena WebDev or LiveCodeBench (Vals AI), where it leads.

Which is cheaper, Gemini 3.5 Flash or GLM 4.7?

Gemini 3.5 Flash costs $1.50 per million input tokens and $9.00 per million output tokens; GLM 4.7 costs $0.60 and $2.20. Per solved SWE-bench Verified task, Gemini 3.5 Flash costs $1.21 and GLM 4.7 $0.29.

Which has the bigger context window, Gemini 3.5 Flash or GLM 4.7?

Gemini 3.5 Flash, with 1.05M tokens against 205K.

How many benchmarks is the Gemini 3.5 Flash vs GLM 4.7 comparison based on?

68 benchmarks have a result for both models as of October 22, 2026. Gemini 3.5 Flash's coding index rests on 4 independent benchmarks and GLM 4.7's on 2. Where only one model has a result, the page says so instead of naming a winner.

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<p style="font:13px/1.4 sans-serif;margin:6px 0 0">Source: <a href="https://llmbenchmark.ai/compare/gemini-3-5-flash-vs-glm-4-7">LLMBenchmark.ai, Gemini 3.5 Flash vs GLM 4.7</a></p>