Llama 3.3 70B Instruct vs Qwen3.8 Omni Flash
As of November 12, 2026, Qwen3.8 Omni Flash costs 25% more per input token than Llama 3.3 70B Instruct ($0.15 vs $0.12 per 1M) and has a 992K-token context window versus 128K.
Llama 3.3 70B Instruct vs Qwen3.8 Omni Flash side by side
| Llama 3.3 70B Instruct | Qwen3.8 Omni Flash | |
|---|---|---|
| Input, per 1M tokens | $0.12 | $0.15 |
| Output, per 1M tokens | $0.30 | $0.47 |
| Cache read, per 1M tokens | – | $0.016 |
| Cache write, per 1M tokens | – | – |
| Context window | 131,072 tokens | 991,808 tokens |
| Max output | 131,072 tokens | 131,072 tokens |
| Image input | – | Yes |
| Tool calling | Yes | Yes |
| Structured output (JSON) | Yes | Yes |
| Prompt caching | – | Yes |
| Reasoning | – | Yes |
| Providers on LiteLLM | 20 | 3 |
| Price shown is from | Hyperbolic | Alibaba Cloud Model Studio |
| Added to LiteLLM | December 2024 | September 18, 2026 |
Cheapest provider for each
- Llama 3.3 70B Instruct: Hyperbolic at $0.12 input and $0.30 output per 1M tokens (
hyperbolic/meta-llama/Llama-3.3-70B-Instruct) - Qwen3.8 Omni Flash: Alibaba Cloud Model Studio at $0.15 input and $0.47 output per 1M tokens (
dashscope/qwen3.8-omni-flash)
When to pick which
Pick Llama 3.3 70B Instruct for
- Costs 20% less per input token: $0.12 vs $0.15 per 1M.
- Available from 20 providers through LiteLLM, vs 3.
Pick Qwen3.8 Omni Flash for
- Larger context window: 991,808 vs 131,072 tokens.
From differences in the data above only; where the data is equal, no reason is given.
Call Llama 3.3 70B Instruct or Qwen3.8 Omni Flash with LiteLLM
Both use the OpenAI format through LiteLLM, so switching is a model name change. Set each provider's API key in the environment first.
from litellm import completion
messages = [{"role": "user", "content": "Hello!"}]
# Llama 3.3 70B Instruct
response = completion(model="hyperbolic/meta-llama/Llama-3.3-70B-Instruct", messages=messages)
# Qwen3.8 Omni Flash
response = completion(model="dashscope/qwen3.8-omni-flash", messages=messages)
# Llama 3.3 70B Instruct, falling back to Qwen3.8 Omni Flash if the call fails
response = completion(model="hyperbolic/meta-llama/Llama-3.3-70B-Instruct", messages=messages, fallbacks=["dashscope/qwen3.8-omni-flash"])FAQ
Which is cheaper, Llama 3.3 70B Instruct or Qwen3.8 Omni Flash?
Llama 3.3 70B Instruct is cheaper at 3 input tokens per output token: $0.165 per 1M tokens blended ($0.12 input, $0.30 output), versus $0.23 ($0.15 and $0.47) for Qwen3.8 Omni Flash, as of November 12, 2026.
Which has the larger context window, Llama 3.3 70B Instruct or Qwen3.8 Omni Flash?
Qwen3.8 Omni Flash has the larger context window: 991,808 tokens, versus 131,072 for Llama 3.3 70B Instruct.
Can I switch from Llama 3.3 70B Instruct to Qwen3.8 Omni Flash without changing my code?
Yes. Through LiteLLM both use the OpenAI format, so switching means changing the model name from "hyperbolic/meta-llama/Llama-3.3-70B-Instruct" to "dashscope/qwen3.8-omni-flash", or pointing a LiteLLM gateway model alias at the other model. You can also use one as a fallback for the other.