Qwen3 VL 8B Instruct vs Llama 3.1 Euryale 70B v2.2
Qwen3 VL 8B Instruct and Llama 3.1 Euryale 70B v2.2 cost about the same per output token ($0.75 vs $0.85 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.75
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.85
Qwen3 VL 8B Instruct 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 Qwen3 VL 8B Instruct for output tokens 1.1x cheaper ($0.75 vs $0.85 per million) and a larger context window (262K against 131K tokens).
No benchmark has tested both yet. Qwen3 VL 8B Instruct has results on LiveCodeBench (Artificial Analysis run), Terminal-Bench Hard (Artificial Analysis run) 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 Qwen3 VL 8B Instruct has a AA Intelligence Index result, 7.3.
Directional only
Long documents
Listed context window
Qwen3 VL 8B Instruct
Qwen3 VL 8B Instruct takes 262K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 VL 8B Instruct
Qwen3 VL 8B Instruct $0.0006, Llama 3.1 Euryale 70B v2.2 $0.0013 per turn.
List prices
At a glance
- Coding index
- Qwen3 VL 8B Instruct 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
- Qwen3 VL 8B Instruct $0.75 against Llama 3.1 Euryale 70B v2.2 $0.85 per million output tokens, 1.1x apart.
- Context window
- Qwen3 VL 8B Instruct takes 262K 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 Index2 boards measured
| Benchmark | Qwen3 VL 8B Instruct | Llama 3.1 Euryale 70B v2.2 | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | 35.3% | Not measured | Only Qwen3 VL 8B Instruct measured |
| Terminal-Bench Hard (Artificial Analysis run) | 3.8% | Not measured | Only Qwen3 VL 8B Instruct 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 | Qwen3 VL 8B Instruct | Llama 3.1 Euryale 70B v2.2 | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.3 | Not measured | Only Qwen3 VL 8B Instruct 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 | Qwen3 VL 8B Instruct | Llama 3.1 Euryale 70B v2.2 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 29.2% | Not measured | Only Qwen3 VL 8B Instruct 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 | Qwen3 VL 8B Instruct | Llama 3.1 Euryale 70B v2.2 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 57.9% | Not measured | Only Qwen3 VL 8B Instruct measured |
| Humanity's Last Exam (Artificial Analysis run) | 3.8% | Not measured | Only Qwen3 VL 8B Instruct measured |
| Neither model has a result on the other 21 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Qwen3 VL 8B Instruct | Llama 3.1 Euryale 70B v2.2 | Leader |
|---|---|---|---|
| AA Math Index | 30.7 | Not measured | Only Qwen3 VL 8B Instruct measured |
| AIME 2025 (Artificial Analysis run) | 30.7% | Not measured | Only Qwen3 VL 8B Instruct measured |
| Neither model has a result on the other 12 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Qwen3 VL 8B Instruct | Llama 3.1 Euryale 70B v2.2 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 74.9% | Not measured | Only Qwen3 VL 8B Instruct measured |
| Neither model has a result on the other 19 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Qwen3 VL 8B Instruct | Llama 3.1 Euryale 70B v2.2 | Leader |
|---|---|---|---|
| IFBench | 39.9% | Not measured | Only Qwen3 VL 8B Instruct measured |
| Long context reasoning (AA-LCR) | 33.3% | Not measured | Only Qwen3 VL 8B Instruct measured |
| Neither model has a result on the other 46 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
Qwen3 VL 8B Instruct costs less
Repository review
50K input + 3K output tokens
Qwen3 VL 8B Instruct costs less
Agent loop
200K input + 10K output tokens
Qwen3 VL 8B Instruct 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 | Qwen3 VL 8B Instruct | Sao10K Llama 3.1 Euryale 70B v2.2 |
|---|---|---|
| Maker | Qwen | Sao10K |
| Weights | Open weights | Open weights |
| Released | October 14, 2025 | August 28, 2024 |
| Context window | 262K | 131K |
| Input $/Mtok | $0.25 | $0.85 |
| Output $/Mtok | $0.75 | $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 Qwen3 VL 8B Instruct 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 Qwen3 VL 8B Instruct for output tokens 1.1x cheaper ($0.75 vs $0.85 per million) and a larger context window (262K against 131K tokens).
Which is cheaper, Qwen3 VL 8B Instruct or Llama 3.1 Euryale 70B v2.2?
Qwen3 VL 8B Instruct costs $0.25 per million input tokens and $0.75 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, Qwen3 VL 8B Instruct or Llama 3.1 Euryale 70B v2.2?
Qwen3 VL 8B Instruct, with 262K tokens against 131K.
Has any benchmark tested both Qwen3 VL 8B Instruct and Llama 3.1 Euryale 70B v2.2?
No benchmark has tested both yet. Qwen3 VL 8B Instruct has results on LiveCodeBench (Artificial Analysis run), Terminal-Bench Hard (Artificial Analysis run) 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 22, 2026.