DeepSeek V3 vs Llama 3.1 Euryale 70B v2.2
DeepSeek V3 and Llama 3.1 Euryale 70B v2.2 cost about the same per output token ($0.9594 vs $0.85 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.9594
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.85
DeepSeek V3 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 DeepSeek V3 for a larger context window (164K against 131K tokens).
No benchmark has tested both yet. DeepSeek V3 has results on Aider polyglot, SWE-bench Lite and GPQA Diamond (Epoch AI run); 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
Web design
Design Arena: websites
No call
Only DeepSeek V3 has a Design Arena: websites result, 1120.
Directional only
Long documents
Listed context window
DeepSeek V3
DeepSeek V3 takes 164K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V3
DeepSeek V3 $0.0008, Llama 3.1 Euryale 70B v2.2 $0.0013 per turn.
List prices
At a glance
- Coding index
- DeepSeek V3 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
- DeepSeek V3 $0.9594 against Llama 3.1 Euryale 70B v2.2 $0.85 per million output tokens, 1.1x apart.
- Context window
- DeepSeek V3 takes 164K 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 | DeepSeek V3 | Llama 3.1 Euryale 70B v2.2 | Leader |
|---|---|---|---|
| Aider polyglot | 70.2% | Not measured | Only DeepSeek V3 measured |
| SWE-bench Lite | 30.7% | Not measured | Only DeepSeek V3 measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | DeepSeek V3 | Llama 3.1 Euryale 70B v2.2 | Leader |
|---|---|---|---|
| GPQA Diamond (Epoch AI run) | 71.2% | Not measured | Only DeepSeek V3 measured |
| Kagi LLM Benchmark | 52.3% | Not measured | Only DeepSeek V3 measured |
| Neither model has a result on the other 20 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | DeepSeek V3 | Llama 3.1 Euryale 70B v2.2 | Leader |
|---|---|---|---|
| OTIS Mock AIME | 48.9% | Not measured | Only DeepSeek V3 measured |
| Neither model has a result on the other 12 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
| Benchmark | DeepSeek V3 | Llama 3.1 Euryale 70B v2.2 | Leader |
|---|---|---|---|
| Design Arena: websites | 1120 | Not measured | Only DeepSeek V3 measured |
| Design Arena: UI components | 1090 | Not measured | Only DeepSeek V3 measured |
| Design Arena: game dev | 1053 | Not measured | Only DeepSeek V3 measured |
| Design Arena: data viz | 1089 | Not measured | Only DeepSeek V3 measured |
| Design Arena: 3D | 1086 | Not measured | Only DeepSeek V3 measured |
| Design Arena: all code categories | 1108 | Not measured | Only DeepSeek V3 measured |
| Design Arena: SVG | 976 | Not measured | Only DeepSeek V3 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
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
DeepSeek V3 costs less
Repository review
50K input + 3K output tokens
DeepSeek V3 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V3 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 | DeepSeek V3 | Sao10K Llama 3.1 Euryale 70B v2.2 |
|---|---|---|
| Maker | DeepSeek | Sao10K |
| Weights | Open weights | Open weights |
| Released | December 26, 2024 | August 28, 2024 |
| Context window | 164K | 131K |
| Input $/Mtok | $0.2887 | $0.85 |
| Output $/Mtok | $0.9594 | $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 DeepSeek V3 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 DeepSeek V3 for a larger context window (164K against 131K tokens).
Which is cheaper, DeepSeek V3 or Llama 3.1 Euryale 70B v2.2?
DeepSeek V3 costs $0.2887 per million input tokens and $0.9594 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, DeepSeek V3 or Llama 3.1 Euryale 70B v2.2?
DeepSeek V3, with 164K tokens against 131K.
Has any benchmark tested both DeepSeek V3 and Llama 3.1 Euryale 70B v2.2?
No benchmark has tested both yet. DeepSeek V3 has results on Aider polyglot, SWE-bench Lite and GPQA Diamond (Epoch AI run); Llama 3.1 Euryale 70B v2.2 has none yet. Compare them on price and context instead. Data as of October 22, 2026.