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GLM 4.7 Flash vs Llama 3.2 1B Instruct

Updated October 21, 2026. 32 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GLM 4.7 Flash Replace Llama 3.2 1B Instruct
GLM 4.7 FlashZ.ai
Coding
No data
Overall
14.9
Output $/M
$0.40
32
Llama 3.2 1B InstructMeta Llama
Coding
No data
Overall
4.8
Output $/M
$0.201
0
vs boards won of 32 shared

Decision reading

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

GLM 4.7 Flash has the edge where the context window is larger, 200K against 60K tokens.

Model AGLM 4.7 FlashZ.aiNot benchmarkedNo coding benchmarkNo cost per solved taskModel BLlama 3.2 1B InstructMeta LlamaNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
32
GLM 4.7 Flash only
11
Llama 3.2 1B Instruct only
5
Like-for-like categories
7 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
GLM 4.7 Flash not benchmarked, Llama 3.2 1B Instruct not benchmarked.
Widest category
Agentic work, 98.8 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: GLM 4.7 Flash 98.8% against Llama 3.2 1B Instruct 0.0%, 98.8 points apart on a 0 to 100 scale.
Output price
GLM 4.7 Flash $0.40 against Llama 3.2 1B Instruct $0.201 per million output tokens, 2.0x apart.
Context window
GLM 4.7 Flash takes 200K tokens, Llama 3.2 1B Instruct 60K.

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 FlashBarsLlama 3.2 1B InstructTies
Coding30
Intelligence10
Agentic work10
Reasoning60
Math30
Knowledge40
General140
All shared boards320
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
Coding benchmarks for GLM 4.7 Flash and Llama 3.2 1B Instruct
BenchmarkGLM 4.7 FlashLlama 3.2 1B InstructLeader
LiveCodeBench (Artificial Analysis run)Not measured1.9%Only Llama 3.2 1B Instruct measured
Terminal-Bench Hard (Artificial Analysis run)22.0%0.0%GLM 4.7 Flash
LMArena Text: coding13821070GLM 4.7 Flash
LMArena Text: software and IT13891056GLM 4.7 Flash
Neither model has a result on the other 34 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GLM 4.7 Flash and Llama 3.2 1B Instruct
BenchmarkGLM 4.7 FlashLlama 3.2 1B InstructLeader
AA Intelligence Index14.94.8GLM 4.7 Flash
Neither model has a result on the other 1 board here
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for GLM 4.7 Flash and Llama 3.2 1B Instruct
BenchmarkGLM 4.7 FlashLlama 3.2 1B InstructLeader
τ²-Bench Telecom98.8%0.0%GLM 4.7 Flash
BALROGNot measured6.6%Only Llama 3.2 1B Instruct measured
Neither model has a result on the other 37 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
Reasoning benchmarks for GLM 4.7 Flash and Llama 3.2 1B Instruct
BenchmarkGLM 4.7 FlashLlama 3.2 1B InstructLeader
GPQA Diamond (Artificial Analysis run)58.1%19.6%GLM 4.7 Flash
Humanity's Last Exam (Artificial Analysis run)7.6%5.5%GLM 4.7 Flash
GPQA Diamond (Epoch AI run)60.5%23.9%GLM 4.7 Flash
LMArena Text: hard prompts13561044GLM 4.7 Flash
LMArena Text: expert13571007GLM 4.7 Flash
LMArena Text: hard prompts in English13691087GLM 4.7 Flash
Neither model has a result on the other 15 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index30
Math benchmarks for GLM 4.7 Flash and Llama 3.2 1B Instruct
BenchmarkGLM 4.7 FlashLlama 3.2 1B InstructLeader
AA Math IndexNot measured0.0Only Llama 3.2 1B Instruct measured
AIME 2025 (Artificial Analysis run)Not measured0.0%Only Llama 3.2 1B Instruct measured
OTIS Mock AIME58.3%0.6%GLM 4.7 Flash
LMArena Text: math13551086GLM 4.7 Flash
LMArena Text: mathematical work13691098GLM 4.7 Flash
Neither model has a result on the other 8 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
Knowledge benchmarks for GLM 4.7 Flash and Llama 3.2 1B Instruct
BenchmarkGLM 4.7 FlashLlama 3.2 1B InstructLeader
MMLU Pro (Artificial Analysis run)Not measured20.0%Only Llama 3.2 1B Instruct measured
LMArena Text: science13581055GLM 4.7 Flash
LMArena Text: medicine and healthcare13431009GLM 4.7 Flash
LMArena Text: legal and government13571071GLM 4.7 Flash
LMArena Text: business and finance13431000GLM 4.7 Flash
Neither model has a result on the other 12 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Web design benchmarks for GLM 4.7 Flash and Llama 3.2 1B Instruct
BenchmarkGLM 4.7 FlashLlama 3.2 1B InstructLeader
Design Arena: websites1194Not measuredOnly GLM 4.7 Flash measured
Design Arena: UI components1208Not measuredOnly GLM 4.7 Flash measured
Design Arena: game dev1135Not measuredOnly GLM 4.7 Flash measured
Design Arena: data viz1123Not measuredOnly GLM 4.7 Flash measured
Design Arena: 3D1121Not measuredOnly GLM 4.7 Flash measured
Design Arena: all code categories1178Not measuredOnly GLM 4.7 Flash measured
Design Arena: SVG1038Not 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 Llama 3.2 1B Instruct
BenchmarkGLM 4.7 FlashLlama 3.2 1B InstructLeader
IFBench60.8%22.8%GLM 4.7 Flash
Long context reasoning (AA-LCR)41.7%6.7%GLM 4.7 Flash
LMArena Text13511055GLM 4.7 Flash
LMArena Text: instruction following13271031GLM 4.7 Flash
LMArena Text: creative writing12961033GLM 4.7 Flash
LMArena Text: English13731117GLM 4.7 Flash
LMArena Text: non-English1330973GLM 4.7 Flash
LMArena Text: Chinese1403959GLM 4.7 Flash
LMArena Text: German13361014GLM 4.7 Flash
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: Russian1333941GLM 4.7 Flash
LMArena Text: Polish1291Not measuredOnly GLM 4.7 Flash measured
LMArena Text: multi-turn13421030GLM 4.7 Flash
LMArena Text: longer queries13451050GLM 4.7 Flash
LMArena Text: writing and language13201030GLM 4.7 Flash
LMArena Text: entertainment and media13041025GLM 4.7 Flash
Neither model has a result on the other 28 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 Llama 3.2 1B Instruct
BenchmarkGLM 4.7 FlashLlama 3.2 1B InstructLeader
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
Llama 3.2 1B Instruct$0.0001Fits in one request

Llama 3.2 1B Instruct costs less

Repository review

50K input + 3K output tokens

GLM 4.7 Flash$0.0045Fits in one request
Llama 3.2 1B Instruct$0.0020Fits in one request

Llama 3.2 1B Instruct costs less

Agent loop

200K input + 10K output tokens

GLM 4.7 Flash$0.017Exceeds the context window
Llama 3.2 1B Instruct$0.0074Exceeds the context window

Llama 3.2 1B Instruct 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 Llama 3.2 1B Instruct
SpecificationZ.ai GLM 4.7 FlashMeta Llama 3.2 1B Instruct
MakerZ.aiMeta Llama
WeightsOpen weightsOpen weights
ReleasedJanuary 19, 2026September 25, 2024
Context window200K60K
Input $/Mtok$0.0653$0.027
Output $/Mtok$0.40$0.201
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 Llama 3.2 1B Instruct for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. GLM 4.7 Flash has the edge where the context window is larger, 200K against 60K tokens.

Which is cheaper, GLM 4.7 Flash or Llama 3.2 1B Instruct?

GLM 4.7 Flash costs $0.0653 per million input tokens and $0.40 per million output tokens; Llama 3.2 1B Instruct costs $0.027 and $0.201. Cost per solved task is known for neither of them.

Which has the bigger context window, GLM 4.7 Flash or Llama 3.2 1B Instruct?

GLM 4.7 Flash, with 200K tokens against 60K.

How many benchmarks is the GLM 4.7 Flash vs Llama 3.2 1B Instruct comparison based on?

32 benchmarks have a result for both models as of October 21, 2026. GLM 4.7 Flash's coding index rests on 0 independent benchmarks and Llama 3.2 1B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.

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