Model comparison
GPT-5.1-Codex vs Tulu 3 (Tülu 3) 70B
GPT-5.1-Codex is the stronger model overall, scoring 38.6 to 33.0 on the Noometry Index.
Last verified . 0 shared benchmarks.
Allen Institute for AI (Ai2)Summary
- The widest gap is in math, where GPT-5.1-Codex leads 30.3 to 14.2.
- Tulu 3 (Tülu 3) 70B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.1-Codex | Tulu 3 (Tülu 3) 70B | |
|---|---|---|
| Provider | OpenAI | Allen Institute for AI (Ai2) |
| Noometry Index | 38.6 | 33.0 |
| Released | 2025-11-12 | 2024-11-21 |
| Weights | Proprietary | Open |
| Context window | 400K | — |
| Max output | 128K | — |
| Input $ / M tokens | $1.25 | — |
| Output $ / M tokens | $10 | — |
| Results tracked | 6 | 14 |
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Category by category
Coding GPT-5.1-Codex leads
GPT-5.1-Codex: 41.9 (#116), Tulu 3 (Tülu 3) 70B: 36.0 (#214)
| Benchmark | GPT-5.1-Codex | Tulu 3 (Tülu 3) 70B |
|---|---|---|
| SWE-bench Verified (bash only) | 66% | — |
| LMArena WebDev | 1337 | — |
| LMArena Coding | — | 1235 |
| ALE-Bench | 1,245 | — |
Agentic & Tool Use Not comparable
GPT-5.1-Codex: 38.0 (#33), Tulu 3 (Tülu 3) 70B: —
| Benchmark | GPT-5.1-Codex | Tulu 3 (Tülu 3) 70B |
|---|---|---|
| Terminal-Bench | 60.4% | — |
| METR Time Horizons | 70.8% | — |
Reasoning Not comparable
GPT-5.1-Codex: —, Tulu 3 (Tülu 3) 70B: 23.9 (#169)
| Benchmark | GPT-5.1-Codex | Tulu 3 (Tülu 3) 70B |
|---|---|---|
| LMArena Hard Prompts | — | 1220 |
Math GPT-5.1-Codex leads
GPT-5.1-Codex: 30.3 (#235), Tulu 3 (Tülu 3) 70B: 14.2 (#303)
| Benchmark | GPT-5.1-Codex | Tulu 3 (Tülu 3) 70B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 4.4% |
| ProofBench | 9% | — |
| LMArena Math | — | 1242 |
| MATH Level 5 | — | 42.7% |
Knowledge Not comparable
GPT-5.1-Codex: —, Tulu 3 (Tülu 3) 70B: 25.0 (#264)
| Benchmark | GPT-5.1-Codex | Tulu 3 (Tülu 3) 70B |
|---|---|---|
| GPQA Diamond | — | 46.3% |
Multilingual Not comparable
GPT-5.1-Codex: —, Tulu 3 (Tülu 3) 70B: 39.9 (#222)
| Benchmark | GPT-5.1-Codex | Tulu 3 (Tülu 3) 70B |
|---|---|---|
| LMArena Non-English | — | 1236 |
| LMArena Chinese | — | 1249 |
| LMArena Russian | — | 1246 |
Instruction Following Not comparable
GPT-5.1-Codex: —, Tulu 3 (Tülu 3) 70B: 64.8 (#227)
| Benchmark | GPT-5.1-Codex | Tulu 3 (Tülu 3) 70B |
|---|---|---|
| LMArena Instruction Following | — | 1233 |
Long Context Not comparable
GPT-5.1-Codex: —, Tulu 3 (Tülu 3) 70B: 37.1 (#222)
| Benchmark | GPT-5.1-Codex | Tulu 3 (Tülu 3) 70B |
|---|---|---|
| LMArena Longer Query | — | 1224 |
Writing & Preference Not comparable
GPT-5.1-Codex: —, Tulu 3 (Tülu 3) 70B: 45.6 (#223)
| Benchmark | GPT-5.1-Codex | Tulu 3 (Tülu 3) 70B |
|---|---|---|
| LMArena Text | — | 1256 |
| LMArena Creative Writing | — | 1231 |
| LMArena Multi-Turn | — | 1252 |
Frequently asked questions
Is GPT-5.1-Codex better than Tulu 3 (Tülu 3) 70B?
GPT-5.1-Codex is the stronger model overall, scoring 38.6 to 33.0 on the Noometry Index.
Is GPT-5.1-Codex or Tulu 3 (Tülu 3) 70B better for coding?
GPT-5.1-Codex scores higher on coding benchmarks: 41.9 versus 36.0 in the Noometry coding category.
How many benchmarks do GPT-5.1-Codex and Tulu 3 (Tülu 3) 70B share?
0 benchmarks have published results for both models. GPT-5.1-Codex has 6 scored results on Noometry and Tulu 3 (Tülu 3) 70B has 14.