Ling 3.0 Flash VL vs Llama 3.1 Euryale 70B v2.2
Ling 3.0 Flash VL costs 4.7x less per output token than Llama 3.1 Euryale 70B v2.2 ($0.18 vs $0.85 per million). Updated October 23, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.18
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.85
Ling 3.0 Flash VL 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 Ling 3.0 Flash VL for output tokens 4.7x cheaper ($0.18 vs $0.85 per million).
No benchmark has tested both yet. Ling 3.0 Flash VL has results on AA Coding Index, SciCode and AA Intelligence Index; 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 Ling 3.0 Flash VL has a AA Intelligence Index result, 24.6.
Directional only
Agentic work
AA Agentic Index
No call
Only Ling 3.0 Flash VL has a AA Agentic Index result, 28.7.
Directional only
Long documents
Listed context window
No call
Both take 131K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ling 3.0 Flash VL
Ling 3.0 Flash VL $0.0001, Llama 3.1 Euryale 70B v2.2 $0.0013 per turn.
List prices
At a glance
- Coding index
- Ling 3.0 Flash VL 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
- Ling 3.0 Flash VL $0.18 against Llama 3.1 Euryale 70B v2.2 $0.85 per million output tokens, 4.7x apart.
- Context window
- Both take 131K tokens.
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 Index2 boards measured
| Benchmark | Ling 3.0 Flash VL | Llama 3.1 Euryale 70B v2.2 | Leader |
|---|---|---|---|
| AA Coding Index | 57.0 | Not measured | Only Ling 3.0 Flash VL measured |
| SciCode | 44.2% | Not measured | Only Ling 3.0 Flash VL measured |
| Neither model has a result on the other 41 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Ling 3.0 Flash VL | Llama 3.1 Euryale 70B v2.2 | Leader |
|---|---|---|---|
| AA Intelligence Index | 24.6 | Not measured | Only Ling 3.0 Flash VL 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 | Ling 3.0 Flash VL | Llama 3.1 Euryale 70B v2.2 | Leader |
|---|---|---|---|
| AA Agentic Index | 28.7 | Not measured | Only Ling 3.0 Flash VL 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 | Ling 3.0 Flash VL | Llama 3.1 Euryale 70B v2.2 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 86.2% | Not measured | Only Ling 3.0 Flash VL measured |
| Humanity's Last Exam (Artificial Analysis run) | 22.0% | Not measured | Only Ling 3.0 Flash VL 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 | Ling 3.0 Flash VL | Llama 3.1 Euryale 70B v2.2 | Leader |
|---|---|---|---|
| Long context reasoning (AA-LCR) | 78.3% | Not measured | Only Ling 3.0 Flash VL 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
Ling 3.0 Flash VL costs less
Repository review
50K input + 3K output tokens
Ling 3.0 Flash VL costs less
Agent loop
200K input + 10K output tokens
Ling 3.0 Flash VL 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 | inclusionAI Ling 3.0 Flash VL | Sao10K Llama 3.1 Euryale 70B v2.2 |
|---|---|---|
| Maker | inclusionAI | Sao10K |
| Weights | Open weights | Open weights |
| Released | September 10, 2026 | August 28, 2024 |
| Context window | 131K | 131K |
| Input $/Mtok | $0.06 | $0.85 |
| Output $/Mtok | $0.18 | $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 Ling 3.0 Flash VL 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 Ling 3.0 Flash VL for output tokens 4.7x cheaper ($0.18 vs $0.85 per million).
Which is cheaper, Ling 3.0 Flash VL or Llama 3.1 Euryale 70B v2.2?
Ling 3.0 Flash VL costs $0.06 per million input tokens and $0.18 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, Ling 3.0 Flash VL or Llama 3.1 Euryale 70B v2.2?
Neither: both take 131K tokens.
Has any benchmark tested both Ling 3.0 Flash VL and Llama 3.1 Euryale 70B v2.2?
No benchmark has tested both yet. Ling 3.0 Flash VL has results on AA Coding Index, SciCode and AA Intelligence Index; Llama 3.1 Euryale 70B v2.2 has none yet. Compare them on price and context instead. Data as of October 23, 2026.