LongCat 2.0 vs Llama 3.1 Euryale 70B v2.2
Llama 3.1 Euryale 70B v2.2 costs 1.4x less per output token than LongCat 2.0 ($0.85 vs $1.20 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $1.20
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.85
LongCat 2.0 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 LongCat 2.0 for a larger context window (1.05M against 131K tokens).
No benchmark has tested both yet. LongCat 2.0 has results on AA Coding Index, SciCode and ALE-Bench; 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
Intelligence
AA Intelligence Index
No call
Only LongCat 2.0 has a AA Intelligence Index result, 19.1.
Directional only
Agentic work
AA Agentic Index
No call
Only LongCat 2.0 has a AA Agentic Index result, 14.0.
Directional only
Long documents
Listed context window
LongCat 2.0
LongCat 2.0 takes 1.05M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
LongCat 2.0
LongCat 2.0 $0.0009, Llama 3.1 Euryale 70B v2.2 $0.0013 per turn.
List prices
At a glance
- Coding index
- LongCat 2.0 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
- LongCat 2.0 $1.20 against Llama 3.1 Euryale 70B v2.2 $0.85 per million output tokens, 1.4x apart.
- Context window
- LongCat 2.0 takes 1.05M 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.
CodingDirectional onlySix of these boards feed the Vibe Coding Index3 boards measured
| Benchmark | LongCat 2.0 | Llama 3.1 Euryale 70B v2.2 | Leader |
|---|---|---|---|
| AA Coding Index | 45.3 | Not measured | Only LongCat 2.0 measured |
| SciCode | 36.3% | Not measured | Only LongCat 2.0 measured |
| ALE-Bench | 793.5 | Not measured | Only LongCat 2.0 measured |
| Neither model has a result on the other 40 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | LongCat 2.0 | Llama 3.1 Euryale 70B v2.2 | Leader |
|---|---|---|---|
| AA Intelligence Index | 19.1 | Not measured | Only LongCat 2.0 measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | LongCat 2.0 | Llama 3.1 Euryale 70B v2.2 | Leader |
|---|---|---|---|
| AA Agentic Index | 14.0 | Not measured | Only LongCat 2.0 measured |
| Neither model has a result on the other 41 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | LongCat 2.0 | Llama 3.1 Euryale 70B v2.2 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 78.0% | Not measured | Only LongCat 2.0 measured |
| Humanity's Last Exam (Artificial Analysis run) | 33.7% | Not measured | Only LongCat 2.0 measured |
| Neither model has a result on the other 21 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | LongCat 2.0 | Llama 3.1 Euryale 70B v2.2 | Leader |
|---|---|---|---|
| Long context reasoning (AA-LCR) | 65.0% | Not measured | Only LongCat 2.0 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
LongCat 2.0 costs less
Repository review
50K input + 3K output tokens
LongCat 2.0 costs less
Agent loop
200K input + 10K output tokens
LongCat 2.0 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 | Meituan LongCat 2.0 | Sao10K Llama 3.1 Euryale 70B v2.2 |
|---|---|---|
| Maker | Meituan | Sao10K |
| Weights | Open weights | Open weights |
| Released | July 20, 2026 | August 28, 2024 |
| Context window | 1.05M | 131K |
| Input $/Mtok | $0.30 | $0.85 |
| Output $/Mtok | $1.20 | $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 LongCat 2.0 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 LongCat 2.0 for a larger context window (1.05M against 131K tokens).
Which is cheaper, LongCat 2.0 or Llama 3.1 Euryale 70B v2.2?
LongCat 2.0 costs $0.30 per million input tokens and $1.20 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, LongCat 2.0 or Llama 3.1 Euryale 70B v2.2?
LongCat 2.0, with 1.05M tokens against 131K.
Has any benchmark tested both LongCat 2.0 and Llama 3.1 Euryale 70B v2.2?
No benchmark has tested both yet. LongCat 2.0 has results on AA Coding Index, SciCode and ALE-Bench; Llama 3.1 Euryale 70B v2.2 has none yet. Compare them on price and context instead. Data as of October 22, 2026.