Model comparison
Inkling vs Llama 3.1-70B
Inkling is the stronger model overall, scoring 44.1 to 29.4 on the Noometry Index. Llama 3.1-70B costs 6.4× less per token, which makes it the better buy when Inkling's lead doesn't matter for your workload.
Last verified . 24 shared benchmarks.
MetaSummary
- They share 24 benchmarks with published results for both. Inkling scores higher in 9 categories and Llama 3.1-70B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Inkling leads 55.2 to 24.1.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 88.9% for Inkling and 3.6% for Llama 3.1-70B.
- Llama 3.1-70B is cheaper at $0.40 / $0.40 per million input/output tokens, against $1.87 / $4.68 for Inkling.
- Llama 3.1-70B accepts more context: 128K tokens versus 66K.
Side by side
| Inkling | Llama 3.1-70B | |
|---|---|---|
| Provider | Thinking Machines Lab | Meta |
| Noometry Index | 44.1 | 29.4 |
| Released | 2026-07-15 | 2024-07-23 |
| Weights | Open | Open |
| Context window | 66K | 128K |
| Max output | 66K | 4K |
| Input $ / M tokens | $1.87 | $0.40 |
| Output $ / M tokens | $4.68 | $0.40 |
| Results tracked | 41 | 35 |
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Category by category
Coding Inkling leads
Inkling: 34.1 (#229), Llama 3.1-70B: 29.8 (#292)
| Benchmark | Inkling | Llama 3.1-70B |
|---|---|---|
| WeirdML | 32.3% | 9% |
| LMArena Coding | 1463 | 1260 |
| FrontierCode | 14% | — |
| LMArena WebDev | 1412 | — |
| FrontierSWE | 4.1% | — |
| SciCode | 47% | — |
| BigCodeBench Instruct | — | 46.1% |
| BigCodeBench Complete | — | 54.8% |
| ALE-Bench | 946 | — |
Agentic & Tool Use Inkling leads
Inkling: 29.5 (#76), Llama 3.1-70B: 24.9 (#104)
| Benchmark | Inkling | Llama 3.1-70B |
|---|---|---|
| APEX-Agents | 33.8% | — |
| TheAgentCompany | — | 6.9% |
| τ²-bench Banking | 25% | — |
| BALROG | — | 27.9% |
Reasoning Inkling leads
Inkling: 40.6 (#48), Llama 3.1-70B: 20.8 (#215)
| Benchmark | Inkling | Llama 3.1-70B |
|---|---|---|
| LMArena Hard Prompts | 1452 | 1241 |
| DTBench | 87.5% | 60% |
| LMCA | 37.6% | 14.8% |
| Epoch Capabilities Index | 148.6 | 125.91 |
| ARC-AGI-2 | 36.5% | — |
| SimpleBench | 50% | — |
| ARC-AGI-1 | 79.5% | — |
| CritPt | 5.4% | — |
| Chess Puzzles | 21% | — |
Math Inkling leads
Inkling: 31.2 (#198), Llama 3.1-70B: 12.7 (#298)
| Benchmark | Inkling | Llama 3.1-70B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 88.9% | 3.6% |
| LMArena Math | 1482 | 1252 |
| FrontierMath (Tiers 1-3) | 33.3% | — |
| FrontierMath Tier 4 | 4.9% | — |
| ProofBench | 0% | — |
| Omni-MATH | — | 21% |
| MATH Level 5 | — | 36.7% |
Knowledge Inkling leads
Inkling: 55.2 (#44), Llama 3.1-70B: 24.1 (#264)
| Benchmark | Inkling | Llama 3.1-70B |
|---|---|---|
| GPQA Diamond | 88.3% | 44.2% |
| LMArena Expert | 1468 | 1209 |
| SimpleQA Verified | 40.3% | — |
| MMLU-Pro | — | 65.3% |
| GPQA (HELM) | — | 42.6% |
| MMLU | — | 80.1% |
Multilingual Inkling leads
Inkling: 54.2 (#49), Llama 3.1-70B: 38.9 (#220)
| Benchmark | Inkling | Llama 3.1-70B |
|---|---|---|
| LMArena Non-English | 1434 | 1219 |
| LMArena Chinese | 1495 | 1215 |
| LMArena French | 1453 | 1260 |
| LMArena German | 1456 | 1222 |
| LMArena Japanese | 1428 | 1132 |
| LMArena Korean | 1401 | 1140 |
| LMArena Russian | 1430 | 1234 |
| LMArena Spanish | 1442 | 1253 |
Instruction Following Inkling leads
Inkling: 75.3 (#65), Llama 3.1-70B: 65.5 (#218)
| Benchmark | Inkling | Llama 3.1-70B |
|---|---|---|
| LMArena Instruction Following | 1427 | 1231 |
| IFEval | — | 82.1% |
Long Context Inkling leads
Inkling: 43.9 (#79), Llama 3.1-70B: 37.6 (#208)
| Benchmark | Inkling | Llama 3.1-70B |
|---|---|---|
| LMArena Longer Query | 1436 | 1241 |
Writing & Preference Inkling leads
Inkling: 65.5 (#48), Llama 3.1-70B: 35.6 (#262)
| Benchmark | Inkling | Llama 3.1-70B |
|---|---|---|
| LMArena Text | 1443 | 1261 |
| LMArena Creative Writing | 1390 | 1232 |
| EQ-Bench Creative Writing | 1611 | 784 |
| LMArena Multi-Turn | 1436 | 1256 |
| WildBench | — | 75.8% |
| EQ-Bench 4 | 1226 | — |
Frequently asked questions
Is Inkling better than Llama 3.1-70B?
Inkling is the stronger model overall, scoring 44.1 to 29.4 on the Noometry Index. Llama 3.1-70B costs 6.4× less per token, which makes it the better buy when Inkling's lead doesn't matter for your workload.
Which is cheaper, Inkling or Llama 3.1-70B?
Llama 3.1-70B is cheaper. It lists at $0.40 per million input tokens and $0.40 per million output tokens; Inkling lists at $1.87 and $4.68.
Is Inkling or Llama 3.1-70B better for coding?
Inkling scores higher on coding benchmarks: 34.1 versus 29.8 in the Noometry coding category.
Which has the bigger context window?
Llama 3.1-70B does, with 128K tokens against 66K.
How many benchmarks do Inkling and Llama 3.1-70B share?
24 benchmarks have published results for both models. Inkling has 41 scored results on Noometry and Llama 3.1-70B has 35.