Model comparison
Codellama 70b Instruct vs GPT-4o
Codellama 70b Instruct is the stronger model overall, scoring 33.7 to 28.6 on the Noometry Index.
Last verified . 7 shared benchmarks.
MetaSummary
- They share 7 benchmarks with published results for both. Codellama 70b Instruct scores higher in 2 categories and GPT-4o in 3 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GPT-4o leads 52.6 to 33.4.
- The biggest single-benchmark swing is BigCodeBench Complete: 49.6% for Codellama 70b Instruct and 61.1% for GPT-4o.
- Codellama 70b Instruct has downloadable open weights; the other is API-only.
Side by side
| Codellama 70b Instruct | GPT-4o | |
|---|---|---|
| Provider | Meta | OpenAI |
| Noometry Index | 33.7 | 28.6 |
| Released | — | 2024-05-13 |
| Weights | Open | Proprietary |
| Context window | — | 128K |
| Max output | — | 16K |
| Input $ / M tokens | — | $2.50 |
| Output $ / M tokens | — | $10 |
| Results tracked | 7 | 72 |
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Category by category
Coding Codellama 70b Instruct leads
Codellama 70b Instruct: 37.6 (#193), GPT-4o: 24.8 (#328)
| Benchmark | Codellama 70b Instruct | GPT-4o |
|---|---|---|
| BigCodeBench Instruct | 40.7% | 51.1% |
| BigCodeBench Complete | 49.6% | 61.1% |
| HumanEval+ | 65.9% | 87.2% |
| SWE-bench Verified | — | 31% |
| SWE-bench Verified (bash only) | — | 21.6% |
| Aider Polyglot | — | 45.3% |
| GSO | — | 0% |
| WeirdML | — | 25.1% |
| LiveBench Coding | — | 51.4% |
| LMArena Coding | — | 1297 |
| CadEval | — | 26% |
| MBPP+ | — | 72.2% |
Agentic & Tool Use Not comparable
Codellama 70b Instruct: —, GPT-4o: 21.0 (#141)
| Benchmark | Codellama 70b Instruct | GPT-4o |
|---|---|---|
| GDPval | — | 9.9% |
| TheAgentCompany | — | 8.6% |
| Cybench | — | 12.5% |
| BALROG | — | 32.3% |
| LMArena Search | — | 1006 |
| METR Time Horizons | — | 40.8% |
Reasoning Codellama 70b Instruct leads
Codellama 70b Instruct: 20.1 (#242), GPT-4o: 9.4 (#343)
| Benchmark | Codellama 70b Instruct | GPT-4o |
|---|---|---|
| LMArena Hard Prompts | 1052 | 1281 |
| ARC-AGI-2 | — | 0% |
| SimpleBench | — | 17.8% |
| ARC-AGI-1 | — | 4.5% |
| CritPt | — | 0% |
| Chess Puzzles | — | 13% |
| EnigmaEval | — | 0.8% |
| LiveBench Reasoning | — | 55.8% |
| DTBench | — | 64.5% |
| LiveBench Data Analysis | — | 60.9% |
| LMCA | — | 16.6% |
| Epoch Capabilities Index | — | 128.97 |
| ForecastBench | — | 57.7 |
| LiveBench | — | 55.3% |
Math Not comparable
Codellama 70b Instruct: —, GPT-4o: 10.6 (#312)
| Benchmark | Codellama 70b Instruct | GPT-4o |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 0.4% |
| OTIS Mock AIME 2024-2025 | — | 6.4% |
| Omni-MATH | — | 29.3% |
| LiveBench Math | — | 49.5% |
| LMArena Math | — | 1285 |
| MATH Level 5 | — | 53.3% |
| FrontierMath (Feb 2025 set) | — | 0.3% |
Knowledge Not comparable
Codellama 70b Instruct: —, GPT-4o: 28.8 (#242)
| Benchmark | Codellama 70b Instruct | GPT-4o |
|---|---|---|
| GPQA Diamond | — | 49.2% |
| Humanity's Last Exam | — | 2.7% |
| SimpleQA Verified | — | 26% |
| MMLU-Pro | — | 71.3% |
| Confabulations | — | 15.3% |
| Vectara Hallucination Rate | — | 9.6% |
| GPQA (HELM) | — | 52% |
| LMArena Expert | — | 1250 |
| MMLU | — | 88.1% |
Multimodal Not comparable
Codellama 70b Instruct: —, GPT-4o: 34.5 (#91)
| Benchmark | Codellama 70b Instruct | GPT-4o |
|---|---|---|
| LMArena Vision | — | 1137 |
| Video-MME | — | 71.9% |
| GeoBench | — | 71% |
| VPCT | — | 40% |
| ScienceQA | — | 88.5% |
Multilingual GPT-4o leads
Codellama 70b Instruct: 24.8 (#288), GPT-4o: 43.2 (#186)
| Benchmark | Codellama 70b Instruct | GPT-4o |
|---|---|---|
| LMArena Non-English | 992 | 1283 |
| LMArena Chinese | — | 1277 |
| LMArena French | — | 1304 |
| LMArena German | — | 1282 |
| LMArena Japanese | — | 1257 |
| LMArena Korean | — | 1234 |
| LMArena Russian | — | 1286 |
| LMArena Spanish | — | 1292 |
Instruction Following GPT-4o leads
Codellama 70b Instruct: 51.9 (#293), GPT-4o: 66.6 (#207)
| Benchmark | Codellama 70b Instruct | GPT-4o |
|---|---|---|
| LMArena Instruction Following | 1024 | 1278 |
| LiveBench Instruction Following | — | 68.6% |
| IFEval | — | 81.7% |
Long Context Not comparable
Codellama 70b Instruct: —, GPT-4o: 39.4 (#179)
| Benchmark | Codellama 70b Instruct | GPT-4o |
|---|---|---|
| Fiction.LiveBench | — | 66.7% |
| LMArena Longer Query | — | 1289 |
Writing & Preference GPT-4o leads
Codellama 70b Instruct: 33.4 (#277), GPT-4o: 52.6 (#166)
| Benchmark | Codellama 70b Instruct | GPT-4o |
|---|---|---|
| LMArena Text | 1057 | 1300 |
| LMArena Creative Writing | — | 1292 |
| Short-Story Creative Writing | — | 81.8% |
| WildBench | — | 82.8% |
| LMArena Multi-Turn | — | 1302 |
| LiveBench Language | — | 47.6% |
Frequently asked questions
Is Codellama 70b Instruct better than GPT-4o?
Codellama 70b Instruct is the stronger model overall, scoring 33.7 to 28.6 on the Noometry Index.
Is Codellama 70b Instruct or GPT-4o better for coding?
Codellama 70b Instruct scores higher on coding benchmarks: 37.6 versus 24.8 in the Noometry coding category.
How many benchmarks do Codellama 70b Instruct and GPT-4o share?
7 benchmarks have published results for both models. Codellama 70b Instruct has 7 scored results on Noometry and GPT-4o has 72.