GPT-5.1-Codex-Max vs Llama 3.1 Euryale 70B v2.2
Llama 3.1 Euryale 70B v2.2 costs 11.8x less per output token than GPT-5.1-Codex-Max ($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-5.1-Codex-Max 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 GPT-5.1-Codex-Max for a larger context window (400K against 131K tokens).
No benchmark has tested both yet. GPT-5.1-Codex-Max has results on LiveCodeBench (Vals AI), Vibe Code Bench (Vals AI) and Terminal-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
Long documents
Listed context window
GPT-5.1-Codex-Max
GPT-5.1-Codex-Max takes 400K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.1 Euryale 70B v2.2
GPT-5.1-Codex-Max $0.0063, Llama 3.1 Euryale 70B v2.2 $0.0013 per turn.
List prices
At a glance
- Coding index
- GPT-5.1-Codex-Max 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-5.1-Codex-Max $10.00 against Llama 3.1 Euryale 70B v2.2 $0.85 per million output tokens, 11.8x apart.
- Context window
- GPT-5.1-Codex-Max takes 400K 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 Index4 boards measured
| Benchmark | GPT-5.1-Codex-Max | Llama 3.1 Euryale 70B v2.2 | Leader |
|---|---|---|---|
| LiveCodeBench (Vals AI) | 83.6% | Not measured | Only GPT-5.1-Codex-Max measured |
| Vibe Code Bench (Vals AI) | 22.2% | Not measured | Only GPT-5.1-Codex-Max measured |
| Terminal-Bench | 60.4% | Not measured | Only GPT-5.1-Codex-Max measured |
| ALE-Bench | 1228.3 | Not measured | Only GPT-5.1-Codex-Max measured |
| Neither model has a result on the other 35 boards here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-5.1-Codex-Max | Llama 3.1 Euryale 70B v2.2 | Leader |
|---|---|---|---|
| METR time horizons | 70.8% | Not measured | Only GPT-5.1-Codex-Max measured |
| Neither model has a result on the other 40 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-5.1-Codex-Max | Llama 3.1 Euryale 70B v2.2 | Leader |
|---|---|---|---|
| ProofBench | 9.0% | Not measured | Only GPT-5.1-Codex-Max measured |
| Neither model has a result on the other 12 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-5.1-Codex-Max | Sao10K Llama 3.1 Euryale 70B v2.2 |
|---|---|---|
| Maker | OpenAI | Sao10K |
| Weights | No public weights listed | Open weights |
| Released | December 4, 2025 | August 28, 2024 |
| Context window | 400K | 131K |
| Input $/Mtok | $1.25 | $0.85 |
| Output $/Mtok | $10.00 | $0.85 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.1-Codex-Max 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 GPT-5.1-Codex-Max for a larger context window (400K against 131K tokens).
Which is cheaper, GPT-5.1-Codex-Max or Llama 3.1 Euryale 70B v2.2?
GPT-5.1-Codex-Max costs $1.25 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-5.1-Codex-Max or Llama 3.1 Euryale 70B v2.2?
GPT-5.1-Codex-Max, with 400K tokens against 131K.
Has any benchmark tested both GPT-5.1-Codex-Max and Llama 3.1 Euryale 70B v2.2?
No benchmark has tested both yet. GPT-5.1-Codex-Max has results on LiveCodeBench (Vals AI), Vibe Code Bench (Vals AI) and Terminal-Bench; Llama 3.1 Euryale 70B v2.2 has none yet. Compare them on price and context instead. Data as of October 22, 2026.