Model comparison
Claude Fable 5 vs DeepSeek-R1-Distill-Llama-70B
Claude Fable 5 is the stronger model overall, scoring 66.8 to 37.8 on the Noometry Index.
Last verified . 3 shared benchmarks.
DeepSeekSummary
- They share 3 benchmarks with published results for both. Claude Fable 5 scores higher in 6 categories and DeepSeek-R1-Distill-Llama-70B in 0 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in math, where Claude Fable 5 leads 88.5 to 36.0.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 100% for Claude Fable 5 and 51.4% for DeepSeek-R1-Distill-Llama-70B.
- DeepSeek-R1-Distill-Llama-70B has downloadable open weights; the other is API-only.
Side by side
| Claude Fable 5 | DeepSeek-R1-Distill-Llama-70B | |
|---|---|---|
| Provider | Anthropic | DeepSeek |
| Noometry Index | 66.8 | 37.8 |
| Released | 2026-06-07 | 2025-01-20 |
| Weights | Proprietary | Open |
| Context window | 1M | — |
| Max output | 128K | — |
| Input $ / M tokens | $10 | — |
| Output $ / M tokens | $50 | — |
| Results tracked | 62 | 13 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Claude Fable 5 leads
Claude Fable 5: 70.6 (#4), DeepSeek-R1-Distill-Llama-70B: 36.8 (#202)
| Benchmark | Claude Fable 5 | DeepSeek-R1-Distill-Llama-70B |
|---|---|---|
| DeepSWE | 69.9% | — |
| FrontierCode | 53.5% | — |
| LMArena WebDev | 1625 | — |
| FrontierSWE | 47% | — |
| SciCode | 61% | — |
| GSO | 78.4% | — |
| WeirdML | 91.9% | — |
| BigCodeBench Instruct | — | 35.3% |
| LiveBench Coding | — | 51.6% |
| LMArena Coding | 1519 | — |
| MirrorCode | 63.9% | — |
| BigCodeBench Complete | — | 49.9% |
| ALE-Bench | 2,041 | — |
Agentic & Tool Use Not comparable
Claude Fable 5: 54.0 (#2), DeepSeek-R1-Distill-Llama-70B: —
| Benchmark | Claude Fable 5 | DeepSeek-R1-Distill-Llama-70B |
|---|---|---|
| APEX-Agents | 63.6% | — |
| Remote Labor Index | 16.1% | — |
| τ²-bench Banking | 39.7% | — |
| PostTrainBench | 41.8% | — |
| GBAEval | 74.5% | — |
| GDP.pdf | 30% | — |
| LMArena Search | 1230 | — |
| Vending-Bench 2 | 5,680 | — |
Reasoning Claude Fable 5 leads
Claude Fable 5: 76.8 (#6), DeepSeek-R1-Distill-Llama-70B: 24.9 (#156)
| Benchmark | Claude Fable 5 | DeepSeek-R1-Distill-Llama-70B |
|---|---|---|
| Kagi LLM Benchmark | 91.4% | 52.3% |
| ARC-AGI-2 | 89.2% | — |
| SimpleBench | 81.9% | — |
| NYT Connections (extended) | 92.7% | — |
| ARC-AGI-1 | 98.5% | — |
| CritPt | 28.6% | — |
| Chess Puzzles | 41% | — |
| EnigmaEval | 39.3% | — |
| EBR-Bench | 39.5% | — |
| LiveBench Reasoning | — | 67.6% |
| LMArena Hard Prompts | 1508 | — |
| Mystery Game Puzzles | 52% | — |
| DTBench | 98.4% | — |
| LiveBench Data Analysis | — | 55.9% |
| LMCA | 61.1% | — |
| Surface Evolver Bench | 95% | — |
| Bench to the Future 3 | 0.13 | — |
| Epoch Capabilities Index | 162.06 | — |
| LiveBench | — | 54.5% |
Math Claude Fable 5 leads
Claude Fable 5: 88.5 (#5), DeepSeek-R1-Distill-Llama-70B: 36.0 (#176)
| Benchmark | Claude Fable 5 | DeepSeek-R1-Distill-Llama-70B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 100% | 51.4% |
| FrontierMath (Tiers 1-3) | 87% | — |
| FrontierMath Tier 4 | 90.2% | — |
| ProofBench | 95% | — |
| LiveBench Math | — | 58.1% |
| LMArena Math | 1519 | — |
| MATH Level 5 | — | 89.9% |
| FrontierMath Erdős | 0% | — |
Knowledge Claude Fable 5 leads
Claude Fable 5: 62.2 (#25), DeepSeek-R1-Distill-Llama-70B: 30.7 (#225)
| Benchmark | Claude Fable 5 | DeepSeek-R1-Distill-Llama-70B |
|---|---|---|
| GPQA Diamond | 85.9% | 55.7% |
| SimpleQA Verified | 70.7% | — |
| LMArena Expert | 1534 | — |
Multimodal Not comparable
Claude Fable 5: 45.3 (#17), DeepSeek-R1-Distill-Llama-70B: —
| Benchmark | Claude Fable 5 | DeepSeek-R1-Distill-Llama-70B |
|---|---|---|
| LMArena Vision | 1324 | — |
| Blueprint-Bench 2 | 38.6% | — |
| Furniture Assembly | 35.8% | — |
| LMArena Document | 1496 | — |
Multilingual Not comparable
Claude Fable 5: 57.3 (#9), DeepSeek-R1-Distill-Llama-70B: —
| Benchmark | Claude Fable 5 | DeepSeek-R1-Distill-Llama-70B |
|---|---|---|
| LMArena Non-English | 1481 | — |
| LMArena Chinese | 1543 | — |
| LMArena French | 1505 | — |
| LMArena German | 1486 | — |
| LMArena Japanese | 1506 | — |
| LMArena Korean | 1488 | — |
| LMArena Russian | 1504 | — |
| LMArena Spanish | 1498 | — |
Instruction Following Claude Fable 5 leads
Claude Fable 5: 78.6 (#8), DeepSeek-R1-Distill-Llama-70B: 68.2 (#190)
| Benchmark | Claude Fable 5 | DeepSeek-R1-Distill-Llama-70B |
|---|---|---|
| LiveBench Instruction Following | — | 69.9% |
| LMArena Instruction Following | 1502 | — |
Long Context Not comparable
Claude Fable 5: 46.3 (#23), DeepSeek-R1-Distill-Llama-70B: —
| Benchmark | Claude Fable 5 | DeepSeek-R1-Distill-Llama-70B |
|---|---|---|
| LMArena Longer Query | 1509 | — |
Writing & Preference Claude Fable 5 leads
Claude Fable 5: 75.9 (#5), DeepSeek-R1-Distill-Llama-70B: 49.0 (#194)
| Benchmark | Claude Fable 5 | DeepSeek-R1-Distill-Llama-70B |
|---|---|---|
| LMArena Text | 1491 | — |
| LMArena Creative Writing | 1494 | — |
| EQ-Bench Creative Writing | 1943 | — |
| EQ-Bench 4 | 1340 | — |
| LMArena Multi-Turn | 1504 | — |
| LiveBench Language | — | 23.8% |
Frequently asked questions
Is Claude Fable 5 better than DeepSeek-R1-Distill-Llama-70B?
Claude Fable 5 is the stronger model overall, scoring 66.8 to 37.8 on the Noometry Index.
Is Claude Fable 5 or DeepSeek-R1-Distill-Llama-70B better for coding?
Claude Fable 5 scores higher on coding benchmarks: 70.6 versus 36.8 in the Noometry coding category.
How many benchmarks do Claude Fable 5 and DeepSeek-R1-Distill-Llama-70B share?
3 benchmarks have published results for both models. Claude Fable 5 has 62 scored results on Noometry and DeepSeek-R1-Distill-Llama-70B has 13.