AI model comparison
GLM 4.5V vs Llama 3.3 70B Instruct
Pricing, context window, benchmarks, and features compared side by side. Or skip the guesswork and send one prompt to both.
Z.ai
GLM 4.5V
GLM-4.5V is a vision-language foundation model for multimodal agent applications.
- Input / 1M
- $0.60
- Output / 1M
- $1.80
- Context
- 66K
The Meta Llama 3.3 multilingual large language model (LLM) is a pretrained and instruction tuned generative model in 70B (text in/text out).
- Input / 1M
- $0.59
- Output / 1M
- $0.79
- Context
- 131K
At a glance
Quick verdict
How the two models stack up on the things people ask about most.
Lower price
Llama 3.3 70B Instruct
1.3x cheaper for a typical chat
Higher intelligence score
Llama 3.3 70B Instruct
7.7 vs 7.6 on Artificial Analysis
Larger context window
Llama 3.3 70B Instruct
131K vs 66K tokens
Newer release
GLM 4.5V
Released August 11, 2025
Benchmarks
Benchmark scores
Independent scores from Artificial Analysis. Higher is better.
Intelligence index
Coding index
Agentic index
Pricing
GLM 4.5V vs Llama 3.3 70B Instruct API pricing
Per-token API rates. Cheaper option highlighted.
| Metric | ||
|---|---|---|
| Input tokensPer 1M tokens | $0.60 | $0.59 |
| Output tokensPer 1M tokens | $1.80 | $0.79 |
| Cached input (read)Per 1M tokens | $0.11 | $0.30 |
| Cache writePer 1M tokens | Not available | Not available |
What it costs in practice
Estimated cost per 1,000 requests at standard rates. Coding and long-document figures also show the cost when the input is already cached.
Chat message
2K in, 500 out
Coding task
30K in, 4K out
Long document summary
150K in, 2K out
Specs
Context window and capabilities
How much each model can read, how much it can write, and what it accepts as input.
| Metric | ||
|---|---|---|
| Context window | 66K tokens | 131K tokens |
| Max output | 16K tokens | 33K tokens |
| Input types | Image, Text | Text |
| Output types | Text | Text |
| Reasoning effort levels | Not available | Not available |
| Default reasoning effort | Not available | Not available |
| Release date | August 11, 2025 | December 6, 2024 |
Features
Supported features
API features available for each model.
| Metric | ||
|---|---|---|
| Tool calling | Supported | Supported |
| Structured outputs | Not supported | Supported |
| JSON mode | Supported | Supported |
| Reasoning | Supported | Not supported |
| Temperature | Supported | Supported |
| Stop sequences | Supported | Supported |
| Deterministic seed | Supported | Supported |
| Verbosity control | Not supported | Not supported |
Keep exploring
More on these models
Other head-to-heads, the full model page, and the alternatives list for each model.
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- Llama 3.3 70B Instruct vs GPT-3.5 Turbo
Our take
Llama 3.3 70B Instruct is 1.3x cheaper for a typical chat. Llama 3.3 70B Instruct scores higher on the Artificial Analysis Intelligence Index (7.7 vs 7.6). Llama 3.3 70B Instruct has the larger context window (131K vs 66K tokens). The right pick depends on your workload, so the quickest way to settle it is to send the same prompt to both and compare.
FAQ
Frequently asked questions
Is GLM 4.5V or Llama 3.3 70B Instruct cheaper?
Llama 3.3 70B Instruct is cheaper for a typical chat (2K input tokens and 500 output tokens). Llama 3.3 70B Instruct costs $0.59 per 1M input tokens and $0.79 per 1M output tokens, while GLM 4.5V costs $0.60 per 1M input tokens and $1.80 per 1M output tokens.
Which has a bigger context window, GLM 4.5V or Llama 3.3 70B Instruct?
Llama 3.3 70B Instruct supports up to 131K tokens of context, compared with 66K for GLM 4.5V.
Is GLM 4.5V smarter than Llama 3.3 70B Instruct?
On the Artificial Analysis Intelligence Index, GLM 4.5V scores 7.6 and Llama 3.3 70B Instruct scores 7.7, putting Llama 3.3 70B Instruct ahead. Benchmarks don't capture everything, so the best test is running your own prompts through both.
Can I use GLM 4.5V and Llama 3.3 70B Instruct at the same time?
Yes. Shortcut Chat sends one prompt to multiple models at once, so you can see GLM 4.5V and Llama 3.3 70B Instruct answer side by side and keep the better response.
Why choose? Ask both.
Send one prompt to GLM 4.5V and Llama 3.3 70B Instruct at the same time. Compare the answers side by side and keep the best one.