Model comparison
Inkling-Small vs Llama 3.1-70B
Inkling-Small is the stronger model overall, scoring 46.5 to 29.6 on the Noometry Index. Llama 3.1-70B costs 1.6× less per token, which makes it the better buy when Inkling-Small's lead doesn't matter for your workload.
Last verified . 21 shared benchmarks.
MetaSummary
- They share 21 benchmarks with published results for both. Inkling-Small scores higher in 8 categories and Llama 3.1-70B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where Inkling-Small leads 45.1 to 13.5.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 90% for Inkling-Small 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 $0.45 / $1.20 for Inkling-Small.
- Inkling-Small accepts more context: 524K tokens versus 128K.
Side by side
| Inkling-Small | Llama 3.1-70B | |
|---|---|---|
| Provider | Thinking Machines Lab | Meta |
| Noometry Index | 46.5 | 29.6 |
| Released | 2026-07-15 | 2024-07-23 |
| Weights | Open | Open |
| Context window | 524K | 128K |
| Max output | 1.05M | 4K |
| Input $ / M tokens | $0.45 | $0.40 |
| Output $ / M tokens | $1.20 | $0.40 |
| Results tracked | 33 | 35 |
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Category by category
Coding Inkling-Small leads
Inkling-Small: 43.6 (#85), Llama 3.1-70B: 30.3 (#296)
| Benchmark | Inkling-Small | Llama 3.1-70B |
|---|---|---|
| LMArena Coding | 1451 | 1260 |
| LMArena WebDev | 1409 | — |
| SciCode | 48.7% | — |
| WeirdML | — | 9% |
| BigCodeBench Instruct | — | 46.1% |
| BigCodeBench Complete | — | 54.8% |
Agentic & Tool Use Not comparable
Inkling-Small: —, Llama 3.1-70B: 25.1 (#112)
| Benchmark | Inkling-Small | Llama 3.1-70B |
|---|---|---|
| TheAgentCompany | — | 6.9% |
| BALROG | — | 27.9% |
Reasoning Inkling-Small leads
Inkling-Small: 38.6 (#63), Llama 3.1-70B: 21.6 (#220)
| Benchmark | Inkling-Small | Llama 3.1-70B |
|---|---|---|
| LMArena Hard Prompts | 1423 | 1241 |
| Epoch Capabilities Index | 150.15 | 125.92 |
| ARC-AGI-2 | 40.1% | — |
| ARC-AGI-1 | 84% | — |
| CritPt | 8.3% | — |
| Chess Puzzles | 18% | — |
| Mystery Game Puzzles | 6% | — |
| DTBench | — | 60% |
| LMCA | — | 14.8% |
Math Inkling-Small leads
Inkling-Small: 45.1 (#77), Llama 3.1-70B: 13.5 (#304)
| Benchmark | Inkling-Small | Llama 3.1-70B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 90% | 3.6% |
| LMArena Math | 1459 | 1252 |
| FrontierMath (Tiers 1-3) | 46.3% | — |
| FrontierMath Tier 4 | 17.1% | — |
| ProofBench | 6% | — |
| Omni-MATH | — | 21% |
| MATH Level 5 | — | 36.7% |
Knowledge Inkling-Small leads
Inkling-Small: 48.2 (#77), Llama 3.1-70B: 24.2 (#269)
| Benchmark | Inkling-Small | Llama 3.1-70B |
|---|---|---|
| GPQA Diamond | 88.5% | 44.2% |
| LMArena Expert | 1442 | 1209 |
| SimpleQA Verified | 19.1% | — |
| MMLU-Pro | — | 65.3% |
| GPQA (HELM) | — | 42.6% |
| MMLU | — | 80.1% |
Multimodal Not comparable
Inkling-Small: 39.1 (#62), Llama 3.1-70B: —
| Benchmark | Inkling-Small | Llama 3.1-70B |
|---|---|---|
| LMArena Vision | 1235 | — |
Multilingual Inkling-Small leads
Inkling-Small: 51.7 (#104), Llama 3.1-70B: 38.8 (#225)
| Benchmark | Inkling-Small | Llama 3.1-70B |
|---|---|---|
| LMArena Non-English | 1402 | 1219 |
| LMArena Chinese | 1465 | 1215 |
| LMArena French | 1436 | 1261 |
| LMArena German | 1405 | 1222 |
| LMArena Japanese | 1405 | 1132 |
| LMArena Korean | 1363 | 1140 |
| LMArena Russian | 1391 | 1234 |
| LMArena Spanish | 1428 | 1253 |
Instruction Following Inkling-Small leads
Inkling-Small: 73.8 (#114), Llama 3.1-70B: 65.3 (#223)
| Benchmark | Inkling-Small | Llama 3.1-70B |
|---|---|---|
| LMArena Instruction Following | 1399 | 1231 |
| IFEval | — | 82.1% |
Long Context Inkling-Small leads
Inkling-Small: 42.7 (#118), Llama 3.1-70B: 37.6 (#214)
| Benchmark | Inkling-Small | Llama 3.1-70B |
|---|---|---|
| LMArena Longer Query | 1401 | 1241 |
Writing & Preference Inkling-Small leads
Inkling-Small: 59.6 (#107), Llama 3.1-70B: 35.4 (#267)
| Benchmark | Inkling-Small | Llama 3.1-70B |
|---|---|---|
| LMArena Text | 1414 | 1261 |
| LMArena Creative Writing | 1331 | 1232 |
| EQ-Bench Creative Writing | 1491 | 784 |
| LMArena Multi-Turn | 1418 | 1256 |
| WildBench | — | 75.8% |
Frequently asked questions
Is Inkling-Small better than Llama 3.1-70B?
Inkling-Small is the stronger model overall, scoring 46.5 to 29.6 on the Noometry Index. Llama 3.1-70B costs 1.6× less per token, which makes it the better buy when Inkling-Small's lead doesn't matter for your workload.
Which is cheaper, Inkling-Small 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-Small lists at $0.45 and $1.20.
Is Inkling-Small or Llama 3.1-70B better for coding?
Inkling-Small scores higher on coding benchmarks: 43.6 versus 30.3 in the Noometry coding category.
Which has the bigger context window?
Inkling-Small does, with 524K tokens against 128K.
How many benchmarks do Inkling-Small and Llama 3.1-70B share?
21 benchmarks have published results for both models. Inkling-Small has 33 scored results on Noometry and Llama 3.1-70B has 35.