GPT-3.5 Turbo (older v0613) vs Llama 3.1 Euryale 70B v2.2
Llama 3.1 Euryale 70B v2.2 costs 2.4x less per output token than GPT-3.5 Turbo (older v0613) ($0.85 vs $2.00 per million). Updated October 23, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $2.00
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.85
GPT-3.5 Turbo (older v0613) 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 both yet. GPT-3.5 Turbo (older v0613) has results on GSM8K, MMLU and WinoGrande; Llama 3.1 Euryale 70B v2.2 has none yet. Compare them on price and context instead.
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 4K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.1 Euryale 70B v2.2
GPT-3.5 Turbo (older v0613) $0.0020, Llama 3.1 Euryale 70B v2.2 $0.0013 per turn.
List prices
At a glance
- Coding index
- GPT-3.5 Turbo (older v0613) 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 (older v0613) $2.00 against Llama 3.1 Euryale 70B v2.2 $0.85 per million output tokens, 2.4x apart.
- Context window
- GPT-3.5 Turbo (older v0613) takes 4K 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.
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-3.5 Turbo (older v0613) | Llama 3.1 Euryale 70B v2.2 | Leader |
|---|---|---|---|
| GSM8K | 46.9% | Not measured | Only GPT-3.5 Turbo (older v0613) measured |
| Neither model has a result on the other 13 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-3.5 Turbo (older v0613) | Llama 3.1 Euryale 70B v2.2 | Leader |
|---|---|---|---|
| MMLU | 68.9% | Not measured | Only GPT-3.5 Turbo (older v0613) measured |
| Neither model has a result on the other 19 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-3.5 Turbo (older v0613) | Llama 3.1 Euryale 70B v2.2 | Leader |
|---|---|---|---|
| WinoGrande | 81.6% | Not measured | Only GPT-3.5 Turbo (older v0613) measured |
| Neither model has a result on the other 47 boards here | |||
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 (older v0613) | Sao10K Llama 3.1 Euryale 70B v2.2 |
|---|---|---|
| Maker | OpenAI | Sao10K |
| Weights | No public weights listed | Open weights |
| Released | January 25, 2024 | August 28, 2024 |
| Context window | 4K | 131K |
| Input $/Mtok | $1.00 | $0.85 |
| Output $/Mtok | $2.00 | $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 (older v0613) 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 (older v0613) or Llama 3.1 Euryale 70B v2.2?
GPT-3.5 Turbo (older v0613) costs $1.00 per million input tokens and $2.00 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 (older v0613) or Llama 3.1 Euryale 70B v2.2?
Llama 3.1 Euryale 70B v2.2, with 131K tokens against 4K.
Has any benchmark tested both GPT-3.5 Turbo (older v0613) and Llama 3.1 Euryale 70B v2.2?
No benchmark has tested both yet. GPT-3.5 Turbo (older v0613) has results on GSM8K, MMLU and WinoGrande; Llama 3.1 Euryale 70B v2.2 has none yet. Compare them on price and context instead. Data as of October 23, 2026.