GPT-3.5 Turbo 16k vs Llama 3.1 Euryale 70B v2.2
Llama 3.1 Euryale 70B v2.2 costs 4.7x less per output token than GPT-3.5 Turbo 16k ($0.85 vs $4.00 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $4.00
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.85
GPT-3.5 Turbo 16k or Llama 3.1 Euryale 70B v2.2: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
No benchmark has tested either model yet, so price and context are the only way to compare them.
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
Long documents
Listed context window
Llama 3.1 Euryale 70B v2.2
Llama 3.1 Euryale 70B v2.2 takes 131K tokens against 16K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.1 Euryale 70B v2.2
GPT-3.5 Turbo 16k $0.0050, Llama 3.1 Euryale 70B v2.2 $0.0013 per turn.
List prices
At a glance
- Coding index
- GPT-3.5 Turbo 16k not benchmarked, Llama 3.1 Euryale 70B v2.2 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
- GPT-3.5 Turbo 16k $4.00 against Llama 3.1 Euryale 70B v2.2 $0.85 per million output tokens, 4.7x apart.
- Context window
- GPT-3.5 Turbo 16k takes 16K tokens, Llama 3.1 Euryale 70B v2.2 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.
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
Llama 3.1 Euryale 70B v2.2 costs less
Repository review
50K input + 3K output tokens
Llama 3.1 Euryale 70B v2.2 costs less
Agent loop
200K input + 10K output tokens
Llama 3.1 Euryale 70B v2.2 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.
| Specification | OpenAI GPT-3.5 Turbo 16k | Sao10K Llama 3.1 Euryale 70B v2.2 |
|---|---|---|
| Maker | OpenAI | Sao10K |
| Weights | No public weights listed | Open weights |
| Released | August 28, 2023 | August 28, 2024 |
| Context window | 16K | 131K |
| Input $/Mtok | $3.00 | $0.85 |
| Output $/Mtok | $4.00 | $0.85 |
| 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 GPT-3.5 Turbo 16k better than Llama 3.1 Euryale 70B v2.2 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, GPT-3.5 Turbo 16k or Llama 3.1 Euryale 70B v2.2?
GPT-3.5 Turbo 16k costs $3.00 per million input tokens and $4.00 per million output tokens; Llama 3.1 Euryale 70B v2.2 costs $0.85 and $0.85. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-3.5 Turbo 16k or Llama 3.1 Euryale 70B v2.2?
Llama 3.1 Euryale 70B v2.2, with 131K tokens against 16K.
Has any benchmark tested both GPT-3.5 Turbo 16k and Llama 3.1 Euryale 70B v2.2?
No benchmark has tested either model yet, so price and context are the only way to compare them. Data as of October 22, 2026.