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Gemini 3.8 Flash vs GLM 4.7

Updated October 21, 2026. 64 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace Gemini 3.8 Flash Replace GLM 4.7
Gemini 3.8 FlashGoogle
Coding
70.5
Overall
40.9
Output $/M
$4.125
63
GLM 4.7Z.ai
Coding
33.6
Overall
22.2
Output $/M
$1.98
1
vs boards won of 64 shared

Decision reading

Pick Gemini 3.8 Flash for coding: it scores 70.5 on the Vibe Coding Index against 33.6 for GLM 4.7.

Pick GLM 4.7 when output tokens cost 2.1x less, or when a solved task costs $0.29 against $2.74.

Model AGemini 3.8 FlashGoogle70.5/1002 independent benchmarks$2.74 per solved taskModel BGLM 4.7Z.ai33.6/1001 independent benchmark$0.29 per solved task
Shared results
64
Gemini 3.8 Flash only
62
GLM 4.7 only
16
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 3.8 Flash 70.5 from 2 benchmarks, GLM 4.7 33.6 from 1 benchmark.
Widest category
Math, 23.4 points apart on average over 4 shared benchmarks.
Widest single gap
ProofBench: Gemini 3.8 Flash 48.0% against GLM 4.7 6.0%, 42.0 points apart on a 0 to 100 scale.
Output price
Gemini 3.8 Flash $4.125 against GLM 4.7 $1.98 per million output tokens, 2.1x apart.
Context window
Gemini 3.8 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 3.8 FlashBarsGLM 4.7Ties
Coding90
Intelligence20
Agentic work41
Reasoning80
Math40
Knowledge90
Web design80
General190
All shared boards631
CodingLike-for-likeFour of these boards feed the Vibe Coding Index90
Coding benchmarks for Gemini 3.8 Flash and GLM 4.7
BenchmarkGemini 3.8 FlashGLM 4.7Leader
LiveBench coding82.2%Not measuredOnly Gemini 3.8 Flash measured
LMArena WebDev15851435Gemini 3.8 Flash
AA Coding Index76.345.3Gemini 3.8 Flash
LiveCodeBench (Vals AI)89.5%82.2%Gemini 3.8 Flash
SWE-bench Verified (Vals AI)80.0%69.4%Gemini 3.8 Flash
Terminal-Bench 4 (Vals AI)19.7%Not measuredOnly Gemini 3.8 Flash measured
Vibe Code Bench (Vals AI)78.7%Not measuredOnly Gemini 3.8 Flash measured
Code migration (Vals AI)36.5%Not measuredOnly Gemini 3.8 Flash measured
ProgramBench (Vals AI)1.0%Not measuredOnly Gemini 3.8 Flash measured
IOI (Vals AI)56.9%Not measuredOnly Gemini 3.8 Flash measured
Terminal-Bench 2.1 (Vals AI)81.3%Not measuredOnly Gemini 3.8 Flash measured
SWE-bench Multimodal (Vals AI)40.8%Not measuredOnly Gemini 3.8 Flash measured
Vibe Code Bench 1-100 (Vals AI)18.8%Not measuredOnly Gemini 3.8 Flash measured
SkillsBench (Vals AI)58.0%Not measuredOnly Gemini 3.8 Flash measured
CyberBench (Vals AI)43.8%Not measuredOnly Gemini 3.8 Flash measured
LiveCodeBench (Artificial Analysis run)Not measured89.4%Only GLM 4.7 measured
SciCode56.6%Not measuredOnly Gemini 3.8 Flash measured
Terminal-Bench Hard (Artificial Analysis run)Not measured31.8%Only GLM 4.7 measured
Terminal-BenchNot measured33.4%Only GLM 4.7 measured
FrontierCode41.2%Not measuredOnly Gemini 3.8 Flash measured
FrontierSWE19.6%Not measuredOnly Gemini 3.8 Flash measured
WeirdML84.8%Not measuredOnly Gemini 3.8 Flash measured
ALE-Bench1269.7399.5Gemini 3.8 Flash
LMArena Text: coding15081454Gemini 3.8 Flash
LMArena Text: software and IT15071453Gemini 3.8 Flash
LMArena WebDev: React15771420Gemini 3.8 Flash
LMArena WebDev: HTML16161447Gemini 3.8 Flash
LMArena WebDev: from an image1593Not measuredOnly Gemini 3.8 Flash measured
Neither model has a result on the other 10 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for Gemini 3.8 Flash and GLM 4.7
BenchmarkGemini 3.8 FlashGLM 4.7Leader
AA Intelligence Index40.922.2Gemini 3.8 Flash
Epoch Capabilities Index157.4143.6Gemini 3.8 Flash
Agentic workLike-for-likePublic boards, shown as published, kept out of the index41
Agentic work benchmarks for Gemini 3.8 Flash and GLM 4.7
BenchmarkGemini 3.8 FlashGLM 4.7Leader
AA Agentic Index40.2Not measuredOnly Gemini 3.8 Flash measured
Design Arena: agents building web apps1238Not measuredOnly Gemini 3.8 Flash measured
Design Arena: agents building full-stack apps12441050Gemini 3.8 Flash
Design Arena: agents building mobile apps12361105Gemini 3.8 Flash
Design Arena: agents building Android apps10781118GLM 4.7
Design Arena: agents building Godot games12731058Gemini 3.8 Flash
Design Arena: PowerPoint slides1178Not measuredOnly Gemini 3.8 Flash measured
Design Arena: spreadsheets1052Not measuredOnly Gemini 3.8 Flash measured
LiveBench research79.1%Not measuredOnly Gemini 3.8 Flash measured
LiveBench browser64.0%Not measuredOnly Gemini 3.8 Flash measured
LiveBench long agentic30.1%Not measuredOnly Gemini 3.8 Flash measured
SREBench (Vals AI)11.1%Not measuredOnly Gemini 3.8 Flash measured
Finance Agent (Vals AI)61.4%Not measuredOnly Gemini 3.8 Flash measured
Tax Agent Bench (Vals AI)32.1%Not measuredOnly Gemini 3.8 Flash measured
Legal Research Bench (Vals AI)38.9%Not measuredOnly Gemini 3.8 Flash measured
Harvey's Legal Agent Benchmark (Vals AI)12.1%Not measuredOnly Gemini 3.8 Flash measured
Excel Modeling Benchmark (Vals AI)72.2%Not measuredOnly Gemini 3.8 Flash measured
BioMysteryBench (Vals AI)62.2%Not measuredOnly Gemini 3.8 Flash measured
Terminal-Bench-Science (Vals AI)8.6%Not measuredOnly Gemini 3.8 Flash measured
Vals Science Index35.8%Not measuredOnly Gemini 3.8 Flash measured
Time Horizon Index (Vals AI)18.8%Not measuredOnly Gemini 3.8 Flash measured
CUA-bench (Vals AI)4.2%Not measuredOnly Gemini 3.8 Flash measured
τ²-Bench TelecomNot measured95.9%Only GLM 4.7 measured
APEX-Agents64.3%Not measuredOnly Gemini 3.8 Flash measured
Remote Labor Index5.8%Not measuredOnly Gemini 3.8 Flash measured
Vending-Bench 2$5,094$2,377Gemini 3.8 Flash
Neither model has a result on the other 13 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index80
Reasoning benchmarks for Gemini 3.8 Flash and GLM 4.7
BenchmarkGemini 3.8 FlashGLM 4.7Leader
GPQA Diamond94.4%80.0%Gemini 3.8 Flash
LiveBench reasoning89.3%Not measuredOnly Gemini 3.8 Flash measured
MysteryMechanism (Vals AI)36.5%Not measuredOnly Gemini 3.8 Flash measured
MMMU Pro (Vals AI)89.1%Not measuredOnly Gemini 3.8 Flash measured
GPQA Diamond (Artificial Analysis run)95.3%85.9%Gemini 3.8 Flash
Humanity's Last Exam (Artificial Analysis run)47.8%27.4%Gemini 3.8 Flash
GPQA Diamond (Epoch AI run)95.4%83.3%Gemini 3.8 Flash
ARC-AGI98.5%Not measuredOnly Gemini 3.8 Flash measured
ARC-AGI-289.2%Not measuredOnly Gemini 3.8 Flash measured
SimpleBenchNot measured47.7%Only GLM 4.7 measured
Chess puzzles58.0%21.0%Gemini 3.8 Flash
Mystery game puzzles47.0%Not measuredOnly Gemini 3.8 Flash measured
LMArena Text: hard prompts15091443Gemini 3.8 Flash
LMArena Text: expert15261424Gemini 3.8 Flash
LMArena Text: hard prompts in English15041452Gemini 3.8 Flash
LMArena Vision: diagrams1345Not measuredOnly Gemini 3.8 Flash measured
LMArena Vision: homework1351Not measuredOnly Gemini 3.8 Flash measured
Neither model has a result on the other 4 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index40
Math benchmarks for Gemini 3.8 Flash and GLM 4.7
BenchmarkGemini 3.8 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 AIME98.9%83.3%Gemini 3.8 Flash
FrontierMath, tiers 1 to 368.4%Not measuredOnly Gemini 3.8 Flash measured
FrontierMath, tier 422.0%Not measuredOnly Gemini 3.8 Flash measured
ProofBench48.0%6.0%Gemini 3.8 Flash
LMArena Text: math15321424Gemini 3.8 Flash
LMArena Text: mathematical work15191411Gemini 3.8 Flash
Neither model has a result on the other 3 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index90
Knowledge benchmarks for Gemini 3.8 Flash and GLM 4.7
BenchmarkGemini 3.8 FlashGLM 4.7Leader
MMLU Pro90.2%82.7%Gemini 3.8 Flash
LegalBench (Vals AI)87.0%83.4%Gemini 3.8 Flash
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)74.5%68.8%Gemini 3.8 Flash
MortgageTax (Vals AI)65.3%Not measuredOnly Gemini 3.8 Flash measured
MedQA (Vals AI)Not measured93.7%Only GLM 4.7 measured
MedCode (Vals AI)48.1%32.8%Gemini 3.8 Flash
MMLU Pro (Artificial Analysis run)Not measured85.6%Only GLM 4.7 measured
SimpleQA Verified69.7%32.2%Gemini 3.8 Flash
LMArena Text: science15231457Gemini 3.8 Flash
LMArena Text: medicine and healthcare15081469Gemini 3.8 Flash
LMArena Text: legal and government14971444Gemini 3.8 Flash
LMArena Text: business and finance14791429Gemini 3.8 Flash
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 3.8 Flash and GLM 4.7
BenchmarkGemini 3.8 FlashGLM 4.7Leader
Design Arena: websites13011226Gemini 3.8 Flash
Design Arena: UI components13181199Gemini 3.8 Flash
Design Arena: game dev13051189Gemini 3.8 Flash
Design Arena: data viz12691195Gemini 3.8 Flash
Design Arena: 3D12951191Gemini 3.8 Flash
Design Arena: all code categories13041217Gemini 3.8 Flash
Design Arena: SVG12951142Gemini 3.8 Flash
Design Arena: ASCII art12641165Gemini 3.8 Flash
LiveBench design72.7%Not measuredOnly Gemini 3.8 Flash measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index190
General benchmarks for Gemini 3.8 Flash and GLM 4.7
BenchmarkGemini 3.8 FlashGLM 4.7Leader
Vals Index56.0%Not measuredOnly Gemini 3.8 Flash measured
LiveBench data analysis54.0%Not measuredOnly Gemini 3.8 Flash measured
Public Benefits Bench (Vals AI)65.3%Not measuredOnly Gemini 3.8 Flash measured
SAGE (Vals AI)35.1%Not measuredOnly Gemini 3.8 Flash measured
MedScribe (Vals AI)84.5%68.6%Gemini 3.8 Flash
IFBenchNot measured67.9%Only GLM 4.7 measured
Long context reasoning (AA-LCR)84.0%71.0%Gemini 3.8 Flash
DTBench95.7%Not measuredOnly Gemini 3.8 Flash measured
LMCA52.9%Not measuredOnly Gemini 3.8 Flash measured
CL-benchNot measured15.9%Only GLM 4.7 measured
Surface Evolver Bench76.9%Not measuredOnly Gemini 3.8 Flash measured
Furniture assembly31.7%Not measuredOnly Gemini 3.8 Flash measured
CL-bench LifeNot measured10.9%Only GLM 4.7 measured
LMArena Text14991435Gemini 3.8 Flash
LMArena Text: instruction following14921412Gemini 3.8 Flash
LMArena Text: creative writing14881401Gemini 3.8 Flash
LMArena Vision1315Not measuredOnly Gemini 3.8 Flash measured
LMArena Text: English14981453Gemini 3.8 Flash
LMArena Text: non-English14911417Gemini 3.8 Flash
LMArena Text: Chinese15531495Gemini 3.8 Flash
LMArena Text: German15011426Gemini 3.8 Flash
LMArena Text: Japanese15071439Gemini 3.8 Flash
LMArena Text: Korean14641399Gemini 3.8 Flash
LMArena Text: French14961432Gemini 3.8 Flash
LMArena Text: Spanish14871434Gemini 3.8 Flash
LMArena Text: Russian15141423Gemini 3.8 Flash
LMArena Text: Polish14971435Gemini 3.8 Flash
LMArena Text: multi-turn15031446Gemini 3.8 Flash
LMArena Text: longer queries15071432Gemini 3.8 Flash
LMArena Text: writing and language14941411Gemini 3.8 Flash
LMArena Text: entertainment and media14801400Gemini 3.8 Flash
LMArena Vision: English1318Not measuredOnly Gemini 3.8 Flash measured
LMArena Vision: Chinese1416Not measuredOnly Gemini 3.8 Flash measured
LMArena Vision: reading text1333Not measuredOnly Gemini 3.8 Flash measured
LMArena Vision: humor1321Not measuredOnly Gemini 3.8 Flash measured
LMArena Vision: creative writing1299Not measuredOnly Gemini 3.8 Flash measured
Neither model has a result on the other 10 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Gemini 3.8 Flash and GLM 4.7
BenchmarkGemini 3.8 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 3.8 Flash$0.0029Fits in one request
GLM 4.7$0.0015Fits in one request

GLM 4.7 costs less

Repository review

50K input + 3K output tokens

Gemini 3.8 Flash$0.054Fits in one request
GLM 4.7$0.033Fits in one request

GLM 4.7 costs less

Agent loop

200K input + 10K output tokens

Gemini 3.8 Flash$0.206Fits in one request
GLM 4.7$0.128Exceeds 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 3.8 Flash and GLM 4.7
SpecificationGoogle Gemini 3.8 FlashZ.ai GLM 4.7
MakerGoogleZ.ai
WeightsNo public weights listedOpen weights
ReleasedSeptember 2, 2026December 22, 2025
Context window1.05M205K
Input $/Mtok$0.825$0.54
Output $/Mtok$4.125$1.98
Coding index70.533.6
Independent measurements behind it21
Cost per solved task$2.74$0.29
SourceOpenRouter listingOpenRouter listing

Questions

Is Gemini 3.8 Flash better than GLM 4.7 for coding?

Pick Gemini 3.8 Flash for coding: it scores 70.5 on the Vibe Coding Index against 33.6 for GLM 4.7. Pick GLM 4.7 when output tokens cost 2.1x less, or when a solved task costs $0.29 against $2.74.

Which is cheaper, Gemini 3.8 Flash or GLM 4.7?

Gemini 3.8 Flash costs $0.825 per million input tokens and $4.125 per million output tokens; GLM 4.7 costs $0.54 and $1.98. Per solved SWE-bench Verified task, Gemini 3.8 Flash costs $2.74 and GLM 4.7 $0.29.

Which has the bigger context window, Gemini 3.8 Flash or GLM 4.7?

Gemini 3.8 Flash, with 1.05M tokens against 205K.

How many benchmarks is the Gemini 3.8 Flash vs GLM 4.7 comparison based on?

64 benchmarks have a result for both models as of October 21, 2026. Gemini 3.8 Flash's coding index rests on 2 independent benchmarks 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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