Schematron V2 Turbo vs Llama 3.1 Euryale 70B v2.2
Schematron V2 Turbo costs 5.7x less per output token than Llama 3.1 Euryale 70B v2.2 ($0.15 vs $0.85 per million). Updated October 23, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.15
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.85
Schematron V2 Turbo 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.
Pick Schematron V2 Turbo for output tokens 5.7x cheaper ($0.15 vs $0.85 per million).
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 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Schematron V2 Turbo
Schematron V2 Turbo $0.0001, Llama 3.1 Euryale 70B v2.2 $0.0013 per turn.
List prices
At a glance
- Coding index
- Schematron V2 Turbo 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
- Schematron V2 Turbo $0.15 against Llama 3.1 Euryale 70B v2.2 $0.85 per million output tokens, 5.7x apart.
- Context window
- Schematron V2 Turbo takes 128K 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
Schematron V2 Turbo costs less
Repository review
50K input + 3K output tokens
Schematron V2 Turbo costs less
Agent loop
200K input + 10K output tokens
Schematron V2 Turbo 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 | Inference.net Schematron V2 Turbo | Sao10K Llama 3.1 Euryale 70B v2.2 |
|---|---|---|
| Maker | Inference.net | Sao10K |
| Weights | Open weights | Open weights |
| Released | September 12, 2026 | August 28, 2024 |
| Context window | 128K | 131K |
| Input $/Mtok | $0.03 | $0.85 |
| Output $/Mtok | $0.15 | $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 Schematron V2 Turbo 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. Pick Schematron V2 Turbo for output tokens 5.7x cheaper ($0.15 vs $0.85 per million).
Which is cheaper, Schematron V2 Turbo or Llama 3.1 Euryale 70B v2.2?
Schematron V2 Turbo costs $0.03 per million input tokens and $0.15 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, Schematron V2 Turbo or Llama 3.1 Euryale 70B v2.2?
Llama 3.1 Euryale 70B v2.2, with 131K tokens against 128K.
Has any benchmark tested both Schematron V2 Turbo 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 23, 2026.