GPT-4o vs Llama 3.1 Euryale 70B v2.2
Llama 3.1 Euryale 70B v2.2 costs 11.8x less per output token than GPT-4o ($0.85 vs $10.00 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $10.00
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.85
GPT-4o 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.
Compare them on price, context and the boards below.
No benchmark has tested both yet. GPT-4o has results on SWE-bench Verified, SWE-bench Lite and SWE-bench (full test set); 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 GPT-4o has a Design Arena: websites result, 831.
Directional only
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
Llama 3.1 Euryale 70B v2.2
GPT-4o $0.0075, Llama 3.1 Euryale 70B v2.2 $0.0013 per turn.
List prices
At a glance
- Coding index
- GPT-4o 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
- GPT-4o $10.00 against Llama 3.1 Euryale 70B v2.2 $0.85 per million output tokens, 11.8x apart.
- Context window
- GPT-4o 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.
CodingDirectional onlySix of these boards feed the Vibe Coding Index7 boards measured
| Benchmark | GPT-4o | Llama 3.1 Euryale 70B v2.2 | Leader |
|---|---|---|---|
| SWE-bench Verified | 38.8% | Not measured | Only GPT-4o measured |
| SWE-bench Lite | 38.0% | Not measured | Only GPT-4o measured |
| SWE-bench (full test set) | 18.8% | Not measured | Only GPT-4o measured |
| SWE-bench Multimodal | 30.4% | Not measured | Only GPT-4o measured |
| LiveCodeBench (Artificial Analysis run) | 30.9% | Not measured | Only GPT-4o measured |
| Terminal-Bench Hard (Artificial Analysis run) | 8.3% | Not measured | Only GPT-4o measured |
| SciCode | 1.5% | Not measured | Only GPT-4o measured |
| Neither model has a result on the other 32 boards here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GPT-4o | Llama 3.1 Euryale 70B v2.2 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 25.1% | Not measured | Only GPT-4o measured |
| GDPval | 9.9% | Not measured | Only GPT-4o measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | GPT-4o | Llama 3.1 Euryale 70B v2.2 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 54.3% | Not measured | Only GPT-4o measured |
| Humanity's Last Exam (Artificial Analysis run) | 2.4% | Not measured | Only GPT-4o measured |
| ARC-AGI | 4.5% | Not measured | Only GPT-4o measured |
| ARC-AGI-2 | 0.0% | Not measured | Only GPT-4o measured |
| Neither model has a result on the other 18 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GPT-4o | Llama 3.1 Euryale 70B v2.2 | Leader |
|---|---|---|---|
| AA Math Index | 6.0 | Not measured | Only GPT-4o measured |
| AIME 2025 (Artificial Analysis run) | 6.0% | Not measured | Only GPT-4o measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GPT-4o | Llama 3.1 Euryale 70B v2.2 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 74.8% | Not measured | Only GPT-4o measured |
| MMLU | 88.1% | Not measured | Only GPT-4o measured |
| Neither model has a result on the other 16 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | GPT-4o | Llama 3.1 Euryale 70B v2.2 | Leader |
|---|---|---|---|
| Design Arena: websites | 831 | Not measured | Only GPT-4o measured |
| Design Arena: UI components | 883 | Not measured | Only GPT-4o measured |
| Design Arena: game dev | 906 | Not measured | Only GPT-4o measured |
| Design Arena: data viz | 858 | Not measured | Only GPT-4o measured |
| Design Arena: 3D | 868 | Not measured | Only GPT-4o measured |
| Design Arena: all code categories | 860 | Not measured | Only GPT-4o measured |
| Neither model has a result on the other 3 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | GPT-4o | Llama 3.1 Euryale 70B v2.2 | Leader |
|---|---|---|---|
| IFBench | 34.3% | Not measured | Only GPT-4o measured |
| Long context reasoning (AA-LCR) | 49.3% | Not measured | Only GPT-4o measured |
| DTBench | 64.5% | Not measured | Only GPT-4o measured |
| LMCA | 16.6% | Not measured | Only GPT-4o measured |
| Neither model has a result on the other 42 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
Llama 3.1 Euryale 70B v2.2 costs less
Repository review
50K input + 3K output tokens
Llama 3.1 Euryale 70B v2.2 costs less
Agent loop
200K input + 10K output tokens
Llama 3.1 Euryale 70B v2.2 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 | OpenAI GPT-4o | Sao10K Llama 3.1 Euryale 70B v2.2 |
|---|---|---|
| Maker | OpenAI | Sao10K |
| Weights | No public weights listed | Open weights |
| Released | May 13, 2024 | August 28, 2024 |
| Context window | 128K | 131K |
| Input $/Mtok | $2.50 | $0.85 |
| Output $/Mtok | $10.00 | $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 GPT-4o 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. Compare them on price, context and the boards below.
Which is cheaper, GPT-4o or Llama 3.1 Euryale 70B v2.2?
GPT-4o costs $2.50 per million input tokens and $10.00 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, GPT-4o 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 GPT-4o and Llama 3.1 Euryale 70B v2.2?
No benchmark has tested both yet. GPT-4o has results on SWE-bench Verified, SWE-bench Lite and SWE-bench (full test set); Llama 3.1 Euryale 70B v2.2 has none yet. Compare them on price and context instead. Data as of October 22, 2026.