GLM 5.3 FlashX vs Llama 3.2 3B Instruct

Llama 3.2 3B Instruct costs 3.8x less per output token than GLM 5.3 FlashX ($0.3325 vs $1.25 per million). Updated October 21, 2026; no benchmark has tested both models yet.

Replace GLM 5.3 FlashX Replace Llama 3.2 3B Instruct

GLM 5.3 FlashXZ.ai

Coding: No data
Overall: No data
Output $/M: $1.25

Llama 3.2 3B InstructMeta Llama

Coding: No data
Overall: No data
Output $/M: $0.3325

vs $1.25 vs $0.3325 per 1M output tokens. Llama 3.2 3B Instruct is 3.8x cheaper.

GLM 5.3 FlashX or Llama 3.2 3B Instruct: which to pick

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

Pick GLM 5.3 FlashX for a larger context window (1.05M against 131K tokens).

Model AGLM 5.3 FlashXZ.aiNot benchmarkedNo coding benchmarkNo cost per solved task Model BLlama 3.2 3B InstructMeta LlamaNot benchmarkedNo coding benchmarkNo cost per solved task

No benchmark has tested both yet. GLM 5.3 FlashX has none yet; Llama 3.2 3B Instruct on LiveCodeBench (Artificial Analysis run), LMArena Text: coding and LMArena Text: software and IT. Compare them on price and context instead.

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.

Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only Llama 3.2 3B Instruct has a AA Intelligence Index result, 5.7.
Directional only
Long documents
Listed context window
GLM 5.3 FlashX
GLM 5.3 FlashX takes 1.05M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.2 3B Instruct
GLM 5.3 FlashX $0.0010, Llama 3.2 3B Instruct $0.0002 per turn.
List prices

At a glance

Coding index: GLM 5.3 FlashX not benchmarked, Llama 3.2 3B Instruct not benchmarked.
Widest category: None yet: no category has been tested on both models.
Widest single gap: None yet: no benchmark has tested both models.
Output price: GLM 5.3 FlashX $1.25 against Llama 3.2 3B Instruct $0.3325 per million output tokens, 3.8x apart.
Context window: GLM 5.3 FlashX takes 1.05M tokens, Llama 3.2 3B Instruct 131K.

Benchmark by benchmark

Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.

CodingDirectional onlySix of these boards feed the Vibe Coding Index3 boards measured

Coding benchmarks for GLM 5.3 FlashX and Llama 3.2 3B Instruct
Benchmark	GLM 5.3 FlashX	Llama 3.2 3B Instruct	Leader
LiveCodeBench (Artificial Analysis run)	Not measured	8.3%	Only Llama 3.2 3B Instruct measured
LMArena Text: coding	Not measured	1098	Only Llama 3.2 3B Instruct measured
LMArena Text: software and IT	Not measured	1103	Only Llama 3.2 3B Instruct measured
Neither model has a result on the other 36 boards here

IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured

Intelligence benchmarks for GLM 5.3 FlashX and Llama 3.2 3B Instruct
Benchmark	GLM 5.3 FlashX	Llama 3.2 3B Instruct	Leader
AA Intelligence Index	Not measured	5.7	Only Llama 3.2 3B Instruct measured
Neither model has a result on the other 1 board here

Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured

Agentic work benchmarks for GLM 5.3 FlashX and Llama 3.2 3B Instruct
Benchmark	GLM 5.3 FlashX	Llama 3.2 3B Instruct	Leader
τ²-Bench Telecom	Not measured	21.1%	Only Llama 3.2 3B Instruct measured
BALROG	Not measured	10.1%	Only Llama 3.2 3B Instruct measured
Berkeley Function Calling Leaderboard (BFCL)	Not measured	21.9%	Only Llama 3.2 3B Instruct measured
Neither model has a result on the other 38 boards here

ReasoningDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured

Reasoning benchmarks for GLM 5.3 FlashX and Llama 3.2 3B Instruct
Benchmark	GLM 5.3 FlashX	Llama 3.2 3B Instruct	Leader
GPQA Diamond (Artificial Analysis run)	Not measured	25.5%	Only Llama 3.2 3B Instruct measured
Humanity's Last Exam (Artificial Analysis run)	Not measured	5.3%	Only Llama 3.2 3B Instruct measured
LMArena Text: hard prompts	Not measured	1095	Only Llama 3.2 3B Instruct measured
LMArena Text: expert	Not measured	1090	Only Llama 3.2 3B Instruct measured
LMArena Text: hard prompts in English	Not measured	1141	Only Llama 3.2 3B Instruct measured
Neither model has a result on the other 17 boards here

MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured

Math benchmarks for GLM 5.3 FlashX and Llama 3.2 3B Instruct
Benchmark	GLM 5.3 FlashX	Llama 3.2 3B Instruct	Leader
AA Math Index	Not measured	3.3	Only Llama 3.2 3B Instruct measured
AIME 2025 (Artificial Analysis run)	Not measured	3.3%	Only Llama 3.2 3B Instruct measured
LMArena Text: math	Not measured	1126	Only Llama 3.2 3B Instruct measured
LMArena Text: mathematical work	Not measured	1128	Only Llama 3.2 3B Instruct measured
Neither model has a result on the other 9 boards here

KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured

Knowledge benchmarks for GLM 5.3 FlashX and Llama 3.2 3B Instruct
Benchmark	GLM 5.3 FlashX	Llama 3.2 3B Instruct	Leader
MMLU Pro (Artificial Analysis run)	Not measured	34.7%	Only Llama 3.2 3B Instruct measured
LMArena Text: science	Not measured	1106	Only Llama 3.2 3B Instruct measured
LMArena Text: medicine and healthcare	Not measured	1104	Only Llama 3.2 3B Instruct measured
LMArena Text: legal and government	Not measured	1115	Only Llama 3.2 3B Instruct measured
LMArena Text: business and finance	Not measured	1074	Only Llama 3.2 3B Instruct measured
Neither model has a result on the other 13 boards here

GeneralDirectional onlyPublic boards, shown as published, kept out of the index14 boards measured

General benchmarks for GLM 5.3 FlashX and Llama 3.2 3B Instruct
Benchmark	GLM 5.3 FlashX	Llama 3.2 3B Instruct	Leader
IFBench	Not measured	26.2%	Only Llama 3.2 3B Instruct measured
Long context reasoning (AA-LCR)	Not measured	4.3%	Only Llama 3.2 3B Instruct measured
LMArena Text	Not measured	1110	Only Llama 3.2 3B Instruct measured
LMArena Text: instruction following	Not measured	1089	Only Llama 3.2 3B Instruct measured
LMArena Text: creative writing	Not measured	1094	Only Llama 3.2 3B Instruct measured
LMArena Text: English	Not measured	1180	Only Llama 3.2 3B Instruct measured
LMArena Text: non-English	Not measured	1019	Only Llama 3.2 3B Instruct measured
LMArena Text: Chinese	Not measured	1017	Only Llama 3.2 3B Instruct measured
LMArena Text: German	Not measured	1056	Only Llama 3.2 3B Instruct measured
LMArena Text: Russian	Not measured	949	Only Llama 3.2 3B Instruct measured
LMArena Text: multi-turn	Not measured	1105	Only Llama 3.2 3B Instruct measured
LMArena Text: longer queries	Not measured	1100	Only Llama 3.2 3B Instruct measured
LMArena Text: writing and language	Not measured	1096	Only Llama 3.2 3B Instruct measured
LMArena Text: entertainment and media	Not measured	1083	Only Llama 3.2 3B Instruct measured
Neither model has a result on the other 32 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 5.3 FlashX$0.0010Fits in one request

Llama 3.2 3B Instruct$0.0002Fits in one request

Llama 3.2 3B Instruct costs less

Repository review

50K input + 3K output tokens

GLM 5.3 FlashX$0.022Fits in one request

Llama 3.2 3B Instruct$0.0035Fits in one request

Llama 3.2 3B Instruct costs less

Agent loop

200K input + 10K output tokens

GLM 5.3 FlashX$0.086Fits in one request

Llama 3.2 3B Instruct$0.013Exceeds the context window

Llama 3.2 3B 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 5.3 FlashX and Llama 3.2 3B Instruct
Specification	Z.ai GLM 5.3 FlashX	Meta Llama 3.2 3B Instruct
Maker	Z.ai	Meta Llama
Weights	No public weights listed	Open weights
Released	September 18, 2026	September 25, 2024
Context window	1.05M	131K
Input $/Mtok	$0.37	$0.0505
Output $/Mtok	$1.25	$0.3325
Coding index	Not benchmarked	Not benchmarked
Independent measurements behind it	0	0
Cost per solved task	Not computable	Not computable
Source	OpenRouter listing	OpenRouter listing

Questions

Is GLM 5.3 FlashX better than Llama 3.2 3B Instruct for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 5.3 FlashX for a larger context window (1.05M against 131K tokens).

Which is cheaper, GLM 5.3 FlashX or Llama 3.2 3B Instruct?

GLM 5.3 FlashX costs $0.37 per million input tokens and $1.25 per million output tokens; Llama 3.2 3B Instruct costs $0.0505 and $0.3325. Cost per solved task is known for neither of them.

Which has the bigger context window, GLM 5.3 FlashX or Llama 3.2 3B Instruct?

GLM 5.3 FlashX, with 1.05M tokens against 131K.

Has any benchmark tested both GLM 5.3 FlashX and Llama 3.2 3B Instruct?

GLM 5.3 FlashX or Llama 3.2 3B Instruct: which to pick

Which one for your work

Coding

Intelligence

Long documents

Everyday cost

At a glance

Benchmark by benchmark

What each workload costs

Chat turn

Repository review

Agent loop

Specification differences

Questions

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