Model comparison

GLM-4.6V vs Llama 3.1-70B

GLM-4.6V is the stronger model overall, scoring 40.9 to 29.4 on the Noometry Index.

Last verified October 10, 2026. 11 shared benchmarks.

GLM-4.6V Z.ai (Zhipu)

40.9

Rank #133 Confirmed

Llama 3.1-70B

Summary

They share 11 benchmarks with published results for both. GLM-4.6V scores higher in 7 categories and Llama 3.1-70B in 0 categories; 7 gaps are clear of the uncertainty.
The widest gap is in writing & preference, where GLM-4.6V leads 56.8 to 35.6.
Llama 3.1-70B is cheaper at $0.40 / $0.40 per million input/output tokens, against $0.30 / $0.90 for GLM-4.6V.

Side by side

GLM-4.6V and Llama 3.1-70B specifications
	GLM-4.6V	Llama 3.1-70B
Provider	Z.ai (Zhipu)	Meta
Noometry Index	40.9	29.4
Released	2025-12-08	2024-07-23
Weights	Open	Open
Context window	128K	128K
Max output	33K	4K
Input $ / M tokens	$0.30	$0.40
Output $ / M tokens	$0.90	$0.40
Results tracked	12	35

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Category by category

Coding GLM-4.6V leads

GLM-4.6V: 40.2 (#128), Llama 3.1-70B: 29.8 (#292)

Coding benchmarks
Benchmark	GLM-4.6V	Llama 3.1-70B
LMArena Coding	1390	1260
WeirdML	—	9%
BigCodeBench Instruct	—	46.1%
BigCodeBench Complete	—	54.8%

Agentic & Tool Use Not comparable

GLM-4.6V: —, Llama 3.1-70B: 24.9 (#104)

Agentic & Tool Use benchmarks
Benchmark	GLM-4.6V	Llama 3.1-70B
TheAgentCompany	—	6.9%
BALROG	—	27.9%

Reasoning GLM-4.6V leads

GLM-4.6V: 26.5 (#117), Llama 3.1-70B: 20.8 (#215)

Reasoning benchmarks
Benchmark	GLM-4.6V	Llama 3.1-70B
LMArena Hard Prompts	1368	1241
DTBench	—	60%
LMCA	—	14.8%
Epoch Capabilities Index	—	125.91

Math Not comparable

GLM-4.6V: —, Llama 3.1-70B: 12.7 (#298)

Math benchmarks
Benchmark	GLM-4.6V	Llama 3.1-70B
OTIS Mock AIME 2024-2025	—	3.6%
Omni-MATH	—	21%
LMArena Math	—	1252
MATH Level 5	—	36.7%

Knowledge GLM-4.6V leads

GLM-4.6V: 37.4 (#148), Llama 3.1-70B: 24.1 (#264)

Knowledge benchmarks
Benchmark	GLM-4.6V	Llama 3.1-70B
LMArena Expert	1372	1209
GPQA Diamond	—	44.2%
MMLU-Pro	—	65.3%
GPQA (HELM)	—	42.6%
MMLU	—	80.1%

Multimodal Not comparable

GLM-4.6V: 34.2 (#83), Llama 3.1-70B: —

Multimodal benchmarks
Benchmark	GLM-4.6V	Llama 3.1-70B
LMArena Vision	1164	—

Multilingual GLM-4.6V leads

GLM-4.6V: 48.8 (#135), Llama 3.1-70B: 38.9 (#220)

Multilingual benchmarks
Benchmark	GLM-4.6V	Llama 3.1-70B
LMArena Non-English	1359	1219
LMArena Chinese	1426	1215
LMArena Russian	1341	1234
LMArena French	—	1260
LMArena German	—	1222
LMArena Japanese	—	1132
LMArena Korean	—	1140
LMArena Spanish	—	1253

Instruction Following GLM-4.6V leads

GLM-4.6V: 71.6 (#145), Llama 3.1-70B: 65.5 (#218)

Instruction Following benchmarks
Benchmark	GLM-4.6V	Llama 3.1-70B
LMArena Instruction Following	1351	1231
IFEval	—	82.1%

Long Context GLM-4.6V leads

GLM-4.6V: 41.3 (#138), Llama 3.1-70B: 37.6 (#208)

Long Context benchmarks
Benchmark	GLM-4.6V	Llama 3.1-70B
LMArena Longer Query	1358	1241

Writing & Preference GLM-4.6V leads

GLM-4.6V: 56.8 (#132), Llama 3.1-70B: 35.6 (#262)

Writing & Preference benchmarks
Benchmark	GLM-4.6V	Llama 3.1-70B
LMArena Text	1377	1261
LMArena Creative Writing	1347	1232
LMArena Multi-Turn	1359	1256
EQ-Bench Creative Writing	—	784
WildBench	—	75.8%

Frequently asked questions

Is GLM-4.6V better than Llama 3.1-70B?

GLM-4.6V is the stronger model overall, scoring 40.9 to 29.4 on the Noometry Index.

Which is cheaper, GLM-4.6V or Llama 3.1-70B?

Llama 3.1-70B is cheaper. It lists at $0.40 per million input tokens and $0.40 per million output tokens; GLM-4.6V lists at $0.30 and $0.90.

Is GLM-4.6V or Llama 3.1-70B better for coding?

GLM-4.6V scores higher on coding benchmarks: 40.2 versus 29.8 in the Noometry coding category.

Which has the bigger context window?

Both accept 128K tokens.

How many benchmarks do GLM-4.6V and Llama 3.1-70B share?

11 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and Llama 3.1-70B has 35.