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GLM 4.7 Flash vs GPT-4.1 Nano

GLM 4.7 Flash leads GPT-4.1 Nano on the Artificial Analysis Intelligence Index, 14.9 to 7.8, at about the same output price ($0.40 vs $0.40 per million). Updated October 22, 2026; 38 benchmarks have a result for both.

Replace GLM 4.7 Flash Replace GPT-4.1 Nano
GLM 4.7 FlashZ.ai
Coding
No data
Overall
42.1
Output $/M
$0.40
38
GPT-4.1 NanoOpenAI
Coding
No data
Overall
24.7
Output $/M
$0.40
0
vs boards won of 38 shared

GLM 4.7 Flash or GPT-4.1 Nano: which to pick

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

Compare them on price, context and the boards below.

Model AGLM 4.7 FlashZ.aiNot benchmarkedNo coding benchmarkNo cost per solved taskModel BGPT-4.1 NanoOpenAINot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
38
GLM 4.7 Flash only
5
GPT-4.1 Nano only
33
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
GLM 4.7 Flash not benchmarked, GPT-4.1 Nano not benchmarked.
Widest category
Agentic work, 81.6 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: GLM 4.7 Flash 98.8% against GPT-4.1 Nano 17.3%, 81.6 points apart on a 0 to 100 scale.
Output price
Both cost $0.40 per million output tokens.
Context window
GLM 4.7 Flash takes 200K tokens, GPT-4.1 Nano 1.05M.

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
CategoryGLM 4.7 FlashBarsGPT-4.1 NanoTies
Coding30
Intelligence10
Agentic work10
Reasoning60
Math30
Knowledge40
Web design60
General140
All shared boards380
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
Coding benchmarks for GLM 4.7 Flash and GPT-4.1 Nano
BenchmarkGLM 4.7 FlashGPT-4.1 NanoLeader
Aider polyglotNot measured8.9%Only GPT-4.1 Nano measured
AA Coding IndexNot measured11.1Only GPT-4.1 Nano measured
LiveCodeBench (Vals AI)Not measured42.7%Only GPT-4.1 Nano measured
LiveCodeBench (Artificial Analysis run)Not measured32.6%Only GPT-4.1 Nano measured
Terminal-Bench Hard (Artificial Analysis run)22.0%3.8%GLM 4.7 Flash
WeirdMLNot measured19.0%Only GPT-4.1 Nano measured
LMArena Text: coding13821306GLM 4.7 Flash
LMArena Text: software and IT13891305GLM 4.7 Flash
Neither model has a result on the other 31 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GLM 4.7 Flash and GPT-4.1 Nano
BenchmarkGLM 4.7 FlashGPT-4.1 NanoLeader
AA Intelligence Index14.97.8GLM 4.7 Flash
Epoch Capabilities IndexNot measured129.5Only GPT-4.1 Nano measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for GLM 4.7 Flash and GPT-4.1 Nano
BenchmarkGLM 4.7 FlashGPT-4.1 NanoLeader
τ²-Bench Telecom98.8%17.3%GLM 4.7 Flash
Berkeley Function Calling Leaderboard (BFCL)Not measured33.1%Only GPT-4.1 Nano measured
Neither model has a result on the other 39 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
Reasoning benchmarks for GLM 4.7 Flash and GPT-4.1 Nano
BenchmarkGLM 4.7 FlashGPT-4.1 NanoLeader
GPQA DiamondNot measured50.8%Only GPT-4.1 Nano measured
MMMU Pro (Vals AI)Not measured55.0%Only GPT-4.1 Nano measured
GPQA Diamond (Artificial Analysis run)58.1%51.2%GLM 4.7 Flash
Humanity's Last Exam (Artificial Analysis run)7.6%3.8%GLM 4.7 Flash
GPQA Diamond (Epoch AI run)60.5%48.9%GLM 4.7 Flash
ARC-AGINot measured0.0%Only GPT-4.1 Nano measured
ARC-AGI-2Not measured0.0%Only GPT-4.1 Nano measured
Chess puzzlesNot measured0.0%Only GPT-4.1 Nano measured
LMArena Text: hard prompts13561286GLM 4.7 Flash
LMArena Text: expert13571271GLM 4.7 Flash
LMArena Text: hard prompts in English13691293GLM 4.7 Flash
Kagi LLM BenchmarkNot measured33.3%Only GPT-4.1 Nano measured
Neither model has a result on the other 10 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index30
Math benchmarks for GLM 4.7 Flash and GPT-4.1 Nano
BenchmarkGLM 4.7 FlashGPT-4.1 NanoLeader
AIMENot measured26.5%Only GPT-4.1 Nano measured
MATH 500 (Vals AI)Not measured80.2%Only GPT-4.1 Nano measured
MGSM (Vals AI)Not measured69.3%Only GPT-4.1 Nano measured
AA Math IndexNot measured24.0Only GPT-4.1 Nano measured
AIME 2025 (Artificial Analysis run)Not measured24.0%Only GPT-4.1 Nano measured
OTIS Mock AIME58.3%28.9%GLM 4.7 Flash
MATH level 5Not measured70.0%Only GPT-4.1 Nano measured
LMArena Text: math13551274GLM 4.7 Flash
LMArena Text: mathematical work13691274GLM 4.7 Flash
Neither model has a result on the other 4 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
Knowledge benchmarks for GLM 4.7 Flash and GPT-4.1 Nano
BenchmarkGLM 4.7 FlashGPT-4.1 NanoLeader
MMLU ProNot measured63.5%Only GPT-4.1 Nano measured
LegalBench (Vals AI)Not measured61.1%Only GPT-4.1 Nano measured
CorpFin (Vals AI)Not measured42.1%Only GPT-4.1 Nano measured
TaxEval (Vals AI)Not measured60.8%Only GPT-4.1 Nano measured
MortgageTax (Vals AI)Not measured52.8%Only GPT-4.1 Nano measured
MedQA (Vals AI)Not measured68.2%Only GPT-4.1 Nano measured
MMLU Pro (Artificial Analysis run)Not measured65.7%Only GPT-4.1 Nano measured
SimpleQA VerifiedNot measured6.0%Only GPT-4.1 Nano measured
LMArena Text: science13581297GLM 4.7 Flash
LMArena Text: medicine and healthcare13431276GLM 4.7 Flash
LMArena Text: legal and government13571300GLM 4.7 Flash
LMArena Text: business and finance13431267GLM 4.7 Flash
Neither model has a result on the other 6 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index60
Web design benchmarks for GLM 4.7 Flash and GPT-4.1 Nano
BenchmarkGLM 4.7 FlashGPT-4.1 NanoLeader
Design Arena: websites1194972GLM 4.7 Flash
Design Arena: UI components1205913GLM 4.7 Flash
Design Arena: game dev1134970GLM 4.7 Flash
Design Arena: data viz1123891GLM 4.7 Flash
Design Arena: 3D1120924GLM 4.7 Flash
Design Arena: all code categories1177962GLM 4.7 Flash
Design Arena: SVG1037Not measuredOnly GLM 4.7 Flash measured
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index140
General benchmarks for GLM 4.7 Flash and GPT-4.1 Nano
BenchmarkGLM 4.7 FlashGPT-4.1 NanoLeader
IFBench60.8%32.0%GLM 4.7 Flash
Long context reasoning (AA-LCR)41.7%20.3%GLM 4.7 Flash
DTBenchNot measured52.5%Only GPT-4.1 Nano measured
LMCANot measured5.5%Only GPT-4.1 Nano measured
Fiction.LiveBenchNot measured25.0%Only GPT-4.1 Nano measured
LMArena Text13511285GLM 4.7 Flash
LMArena Text: instruction following13271266GLM 4.7 Flash
LMArena Text: creative writing12961260GLM 4.7 Flash
LMArena VisionNot measured1063Only GPT-4.1 Nano measured
LMArena Text: English13731303GLM 4.7 Flash
LMArena Text: non-English13301260GLM 4.7 Flash
LMArena Text: Chinese14031270GLM 4.7 Flash
LMArena Text: German13361288GLM 4.7 Flash
LMArena Text: JapaneseNot measured1198Only GPT-4.1 Nano measured
LMArena Text: Korean1283Not measuredOnly GLM 4.7 Flash measured
LMArena Text: French1332Not measuredOnly GLM 4.7 Flash measured
LMArena Text: Spanish1350Not measuredOnly GLM 4.7 Flash measured
LMArena Text: Russian13331261GLM 4.7 Flash
LMArena Text: Polish1291Not measuredOnly GLM 4.7 Flash measured
LMArena Text: multi-turn13421277GLM 4.7 Flash
LMArena Text: longer queries13451283GLM 4.7 Flash
LMArena Text: writing and language13201261GLM 4.7 Flash
LMArena Text: entertainment and media13041243GLM 4.7 Flash
LMArena Vision: EnglishNot measured1101Only GPT-4.1 Nano measured
Neither model has a result on the other 22 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.7 Flash and GPT-4.1 Nano
BenchmarkGLM 4.7 FlashGPT-4.1 NanoLeader
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

GLM 4.7 Flash$0.0003Fits in one request
GPT-4.1 Nano$0.0003Fits in one request

GLM 4.7 Flash costs less

Repository review

50K input + 3K output tokens

GLM 4.7 Flash$0.0045Fits in one request
GPT-4.1 Nano$0.0062Fits in one request

GLM 4.7 Flash costs less

Agent loop

200K input + 10K output tokens

GLM 4.7 Flash$0.017Exceeds the context window
GPT-4.1 Nano$0.024Fits in one request

GLM 4.7 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 GLM 4.7 Flash and GPT-4.1 Nano
SpecificationZ.ai GLM 4.7 FlashOpenAI GPT-4.1 Nano
MakerZ.aiOpenAI
WeightsOpen weightsNo public weights listed
ReleasedJanuary 19, 2026April 14, 2025
Context window200K1.05M
Input $/Mtok$0.0653$0.10
Output $/Mtok$0.40$0.40
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.7 Flash better than GPT-4.1 Nano for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Compare them on price, context and the boards below.

Which is cheaper, GLM 4.7 Flash or GPT-4.1 Nano?

GLM 4.7 Flash costs $0.0653 per million input tokens and $0.40 per million output tokens; GPT-4.1 Nano costs $0.10 and $0.40. Cost per solved task is known for neither of them.

Which has the bigger context window, GLM 4.7 Flash or GPT-4.1 Nano?

GPT-4.1 Nano, with 1.05M tokens against 200K.

How many benchmarks is the GLM 4.7 Flash vs GPT-4.1 Nano comparison based on?

38 benchmarks have a result for both models as of October 22, 2026. GLM 4.7 Flash's coding index rests on 0 independent benchmarks and GPT-4.1 Nano's on 0. Where only one model has a result, the page says so instead of naming a winner.

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