Pairwise benchmark snapshot · Aug 16, 2026

GLM-5.2 vs Llama 3.3 70B benchmark

Name: GLM-5.2 vs Llama 3.3 70B AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares GLM-5.2 (Zhipu) and Llama 3.3 70B (Meta) using the latest snapshots we have as of Aug 16, 2026. On preference Elo, GLM-5.2 is ahead by 207 points (1,492 vs 1,285; seed-bootstrap (Aug 1, 2026)). On SWE-bench coding, GLM-5.2 resolves 69.3% versus 45.1% (seed-bootstrap (Aug 1, 2026)). Llama 3.3 70B streams faster (140 tok/s) while Llama 3.3 70B is the cheaper output token ($0.32/1M). GLM-5.2 has the larger context window (1M tokens). Every cell below is a dated snapshot from a named public source — we do not invent missing scores, and we do not run SWE-bench ourselves.

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Zhipu

GLM-5.2

Zhipu flagship. Strong Chinese/English coding and agents.

Elo 1,492$0.9680000000000001/1M out

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Model B

Quick vs:vs Claude Opus 4.5 vs Claude Opus 4.6 vs Claude Sonnet 4.5 vs Claude 3.7 Sonnet

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

GLM-5.2 (6)vsLlama 3.3 70B (4)

GLM-5.2: 6W (60%)Overall: GLM-5.2Llama 3.3 70B: 4W (40%)

← GLM-5.2Llama 3.3 70B →

GLM-5.2 (5/5)

Intelligence & Reasoning

Preference Elo, Coding proficiency, SWE-bench & LiveBench accuracy

Preference Elo

1,492vs1,285

Coding Elo

1,504vs—

SWE-bench

69.3%vs45.1%

LiveBench

65.2%vs49.8%

GPQA Diamond

79.5%vs65.7%

GLM-5.2: 5WLlama 3.3 70B: 0W

Llama 3.3 70B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

84 tok/svs140 tok/s

Time to first token

270 msvs160 ms

GLM-5.2: 0WLlama 3.3 70B: 2W

Llama 3.3 70B (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.97/1Mvs$0.32/1M

Input price

$0.31/1Mvs$0.1/1M

Context window

1Mvs131k

GLM-5.2: 1WLlama 3.3 70B: 2W

Capability Matchup Radar

Multi-Dimensional Capability Radar

Each spoke is a skill. Farther from the center is better (0–100th percentile). Tap any dot to inspect details.

Tap any node to inspect

Percentile 0–100

Dimensional AdvantageGLM-5.2 4/6

Preference Elo

GLM-5.2

207 pts advantage

Throughput Speed

Llama 3.3 70B

56 tok/s faster

Price Efficiency

Llama 3.3 70B

$0.65/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

+16%1,492

EloHigh is better

1,285

Code Elo

High is better

1,504

Code EloHigh is better

—

LiveBench

High is better

+31%65.2%

LiveBenchHigh is better

49.8%

SWE-bench

High is better

+54%69.3%

SWE-benchHigh is better

45.1%

GPQA

High is better

+21%79.5%

GPQAHigh is better

65.7%

TTFT

Low is better

270 ms

TTFTLow is better

160 ms+69%

Speed

High is better

84 tok/s

SpeedHigh is better

140 tok/s+67%

In $

Low is better

$0.31/1M

In $Low is better

$0.1/1M+208%

Out $

Low is better

$0.97/1M

Out $Low is better

$0.32/1M+203%

Context

High is better

+700%1M

ContextHigh is better

131k

Interactive Simulator

Workload Cost & Savings Calculator

Simulate monthly production API costs based on published $/1M tokens.

Save up to 67% with Llama 3.3 70B

Monthly Volume50M tokens/mo

Quick Presets:

Token Ratio (In vs Out)70% In / 30% Out

RAG / Search (10% out)Coding / Chat (50% out)

GLM-5.2$25.30 / mo

In: $10.78Out: $14.52

Llama 3.3 70B$8.30 / mo

In: $3.50Out: $4.80

Estimated Cost Delta

Llama 3.3 70B is estimated to save $17.00/month ($204/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
GLM-5.2:Pick GLM-5.2 when repo-level coding accuracy is the constraint.
2
Llama 3.3 70B:Pick Llama 3.3 70B when you are optimizing output cost.
3
Llama 3.3 70B:Pick Llama 3.3 70B when time-to-first-token matters more than peak Elo.

Recommended Workload Routing

Repo / coding agentsGLM-5.2

Higher SWE-bench (69.3%).

High-volume chatLlama 3.3 70B

Lower output list price ($0.32/1M).

Voice / low-latency UILlama 3.3 70B

Lower TTFT (160 ms).

Long-document RAGGLM-5.2

Larger window (1M).

Screenshots / visionGLM-5.2

GLM-5.2 is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	GLM-5.2	Higher SWE-bench (69.3%).
High-volume chat	Llama 3.3 70B	Lower output list price ($0.32/1M).
Voice / low-latency UI	Llama 3.3 70B	Lower TTFT (160 ms).
Long-document RAG	GLM-5.2	Larger window (1M).
Screenshots / vision	GLM-5.2	GLM-5.2 is the side marked multimodal in the catalog.

Related Editorial Dispatches & Benchmark Notes

View all news

newsAug 16, 2026

GLM-5.2 vs GLM 5V Turbo Price and SWE-bench 2026

GLM-5.2 undercuts GLM 5V Turbo by 74.2% on input at $0.31/1M, with 1,492 Elo and 69.3% SWE-bench in dated 2026 snapshots.

launchJun 18, 2026

GLM-5.2 remains the prior Zhipu flagship for upgrade pairs

Strong Chinese/English coding. 5.3 is the new row; 5.2 stays so the delta is visible.

launchDec 6, 2024

Llama 3.3 70B stays as the previous Meta 70B workhorse

Dec 2024 instruct 70B. Still a self-host baseline. Llama 4 is the 2026 buy.

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LIVE

Zhipu

GLM-5.2

Elo 1,492

LiveBench65.2%

SWE-bench69.3%

Speed84 tok/s

Output cost$0.97/1M

Llama 3.3 70B

Elo 1,285

LiveBench49.8%

SWE-bench45.1%

Speed140 tok/s

Output cost$0.32/1M

Frequently Asked Questions

Which is better overall, GLM-5.2 or Llama 3.3 70B?: GLM-5.2 has the higher preference Elo in our latest snapshot (1,492). “Better” still depends on coding, price, and latency — see the table.
Which is better at coding, GLM-5.2 or Llama 3.3 70B?: GLM-5.2 leads SWE-bench at 69.3% vs 45.1%. SWE-bench is one harness, not your repo.
Which is cheaper to run in production?: Llama 3.3 70B output tokens are $0.32/1M versus $0.97/1M. Input prices and retry rates still move the real bill.
Which is faster, GLM-5.2 or Llama 3.3 70B?: Llama 3.3 70B has the lower time-to-first-token (160 ms vs 270 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: GLM-5.2 accepts 1M tokens versus 131k.
Can I self-host either model?: Both GLM-5.2 and Llama 3.3 70B are marked open-weights. Hosting cost is not in this table.
Are these scores from CompareLLM’s own evals?: No. V1 aggregates public leaderboards (and optional first-party latency pings). Each cell names its source and date. Read /methodology.
What does preference Elo mean on this page?: Preference Elo is a crowd vote from LMArena / Arena. People see two hidden answers and pick the one they like more. The model that wins more often gets a higher Elo. That means people preferred it — not that it passed a school test. It is not SWE-bench, not accuracy, and not a number we invent. GLM-5.2 and Llama 3.3 70B Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All GLM-5.2 matchups →|All Llama 3.3 70B matchups →

GLM-5.2 vs GLM-5.3 (next glm)Llama 3.3 70B vs Llama 3.1 70B (previous llama)Llama 3.3 70B vs Llama 4 Maverick (next llama)Llama 4 Maverick vs GLM-5.2 Llama 4 Scout vs GLM-5.2 DeepSeek V4 Flash vs GLM-5.2

GLM-5.2 vs Llama 3.3 70B benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

GLM-5.2 (6)vsLlama 3.3 70B (4)

GLM-5.2: 6W (60%)Overall: GLM-5.2Llama 3.3 70B: 4W (40%)

← GLM-5.2Llama 3.3 70B →

GLM-5.2 (5/5)

Intelligence & Reasoning

Preference Elo, Coding proficiency, SWE-bench & LiveBench accuracy

Preference Elo

1,492vs1,285

Coding Elo

1,504vs—

SWE-bench

69.3%vs45.1%

LiveBench

65.2%vs49.8%

GPQA Diamond

79.5%vs65.7%

GLM-5.2: 5WLlama 3.3 70B: 0W

Llama 3.3 70B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

84 tok/svs140 tok/s

Time to first token

270 msvs160 ms

GLM-5.2: 0WLlama 3.3 70B: 2W

Llama 3.3 70B (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.97/1Mvs$0.32/1M

Input price

$0.31/1Mvs$0.1/1M

Context window

1Mvs131k

GLM-5.2: 1WLlama 3.3 70B: 2W

Recommended Workload Routing

Repo / coding agentsGLM-5.2

Higher SWE-bench (69.3%).

High-volume chatLlama 3.3 70B

Lower output list price ($0.32/1M).

Voice / low-latency UILlama 3.3 70B

Lower TTFT (160 ms).

Long-document RAGGLM-5.2

Larger window (1M).

Screenshots / visionGLM-5.2

GLM-5.2 is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	GLM-5.2	Higher SWE-bench (69.3%).
High-volume chat	Llama 3.3 70B	Lower output list price ($0.32/1M).
Voice / low-latency UI	Llama 3.3 70B	Lower TTFT (160 ms).
Long-document RAG	GLM-5.2	Larger window (1M).
Screenshots / vision	GLM-5.2	GLM-5.2 is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, GLM-5.2 or Llama 3.3 70B?

GLM-5.2 has the higher preference Elo in our latest snapshot (1,492). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, GLM-5.2 or Llama 3.3 70B?

GLM-5.2 leads SWE-bench at 69.3% vs 45.1%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

Llama 3.3 70B output tokens are $0.32/1M versus $0.97/1M. Input prices and retry rates still move the real bill.

Which is faster, GLM-5.2 or Llama 3.3 70B?

Llama 3.3 70B has the lower time-to-first-token (160 ms vs 270 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

GLM-5.2 accepts 1M tokens versus 131k.

Can I self-host either model?

Both GLM-5.2 and Llama 3.3 70B are marked open-weights. Hosting cost is not in this table.

Are these scores from CompareLLM’s own evals?

No. V1 aggregates public leaderboards (and optional first-party latency pings). Each cell names its source and date. Read /methodology.

What does preference Elo mean on this page?

Preference Elo is a crowd vote from LMArena / Arena. People see two hidden answers and pick the one they like more. The model that wins more often gets a higher Elo. That means people preferred it — not that it passed a school test. It is not SWE-bench, not accuracy, and not a number we invent. GLM-5.2 and Llama 3.3 70B Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Swap or Add Any Model to this Matchup

Interactive Comparison Diagram

Tug-of-War Matchup Matrix

Intelligence & Reasoning

Speed & Latency

Pricing & Capacity

Multi-Dimensional Capability Radar

GLM-5.2

Llama 3.3 70B

Llama 3.3 70B

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

GLM-5.2 vs GLM 5V Turbo Price and SWE-bench 2026

GLM-5.2 remains the prior Zhipu flagship for upgrade pairs

Llama 3.3 70B stays as the previous Meta 70B workhorse

Cast Your Matchup Vote

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CompareLLM Matrix

GLM-5.2

Llama 3.3 70B

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons

Swap or Add Any Model to this Matchup

Interactive Comparison Diagram

Tug-of-War Matchup Matrix

Intelligence & Reasoning

Speed & Latency

Pricing & Capacity

Multi-Dimensional Capability Radar

GLM-5.2

Llama 3.3 70B

Llama 3.3 70B

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

GLM-5.2 vs GLM 5V Turbo Price and SWE-bench 2026

GLM-5.2 remains the prior Zhipu flagship for upgrade pairs

Llama 3.3 70B stays as the previous Meta 70B workhorse

Cast Your Matchup Vote

Community Discussions (0)

Share card

CompareLLM Matrix

GLM-5.2

Llama 3.3 70B

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons