Model comparison
Inkling vs Llama 4 Scout
Inkling is the stronger model overall, scoring 44.1 to 27.7 on the Noometry Index. Llama 4 Scout costs 17× less per token, which makes it the better buy when Inkling's lead doesn't matter for your workload.
Last verified . 27 shared benchmarks.
MetaSummary
- They share 27 benchmarks with published results for both. Inkling scores higher in 9 categories and Llama 4 Scout in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Inkling leads 40.4 to 9.1.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 88.9% for Inkling and 7.8% for Llama 4 Scout.
- Llama 4 Scout is cheaper at $0.10 / $0.30 per million input/output tokens, against $1.87 / $4.68 for Inkling.
- Llama 4 Scout accepts more context: 128K tokens versus 66K.
Side by side
| Inkling | Llama 4 Scout | |
|---|---|---|
| Provider | Thinking Machines Lab | Meta |
| Noometry Index | 44.1 | 27.7 |
| Released | 2026-07-15 | 2025-04-05 |
| Weights | Open | Open |
| Context window | 66K | 128K |
| Max output | 66K | 4K |
| Input $ / M tokens | $1.87 | $0.10 |
| Output $ / M tokens | $4.68 | $0.30 |
| Results tracked | 41 | 43 |
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Category by category
Coding Inkling leads
Inkling: 34.5 (#234), Llama 4 Scout: 20.2 (#339)
| Benchmark | Inkling | Llama 4 Scout |
|---|---|---|
| SciCode | 47% | 17% |
| LMArena Coding | 1464 | 1286 |
| FrontierCode | 14% | — |
| SWE-bench Verified (bash only) | — | 9.1% |
| LMArena WebDev | 1413 | — |
| FrontierSWE | 4.1% | — |
| WeirdML | 32.3% | — |
| BigCodeBench Complete | — | 43.1% |
| ALE-Bench | 946 | — |
Agentic & Tool Use Inkling leads
Inkling: 29.6 (#85), Llama 4 Scout: 24.6 (#119)
| Benchmark | Inkling | Llama 4 Scout |
|---|---|---|
| APEX-Agents | 33.8% | — |
| Berkeley Function Calling Leaderboard | — | 28.1% |
| τ²-bench Banking | 25% | — |
Reasoning Inkling leads
Inkling: 40.4 (#56), Llama 4 Scout: 9.1 (#345)
| Benchmark | Inkling | Llama 4 Scout |
|---|---|---|
| ARC-AGI-2 | 36.5% | 0% |
| ARC-AGI-1 | 79.5% | 0.5% |
| CritPt | 5.4% | 0% |
| LMArena Hard Prompts | 1451 | 1266 |
| DTBench | 87.5% | 57.9% |
| LMCA | 37.6% | 12% |
| Epoch Capabilities Index | 148.54 | 129.64 |
| SimpleBench | 50% | — |
| Kagi LLM Benchmark | — | 36.9% |
| Chess Puzzles | 21% | — |
| ForecastBench | — | 57.5 |
Math Inkling leads
Inkling: 31.3 (#225), Llama 4 Scout: 19.6 (#286)
| Benchmark | Inkling | Llama 4 Scout |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 88.9% | 7.8% |
| LMArena Math | 1479 | 1287 |
| FrontierMath (Tiers 1-3) | 33.3% | — |
| FrontierMath Tier 4 | 4.9% | — |
| ProofBench | 0% | — |
| Omni-MATH | — | 37.3% |
| MATH Level 5 | — | 62.3% |
| FrontierMath (Feb 2025 set) | — | 0% |
Knowledge Inkling leads
Inkling: 55.1 (#49), Llama 4 Scout: 31.9 (#217)
| Benchmark | Inkling | Llama 4 Scout |
|---|---|---|
| GPQA Diamond | 88.3% | 51.8% |
| LMArena Expert | 1465 | 1235 |
| SimpleQA Verified | 40.3% | — |
| MMLU-Pro | — | 74.2% |
| Vectara Hallucination Rate | — | 7.7% |
| GPQA (HELM) | — | 50.7% |
Multimodal Not comparable
Inkling: —, Llama 4 Scout: 32.2 (#102)
| Benchmark | Inkling | Llama 4 Scout |
|---|---|---|
| LMArena Vision | — | 1118 |
| SpatialViz-Bench | — | 34.2% |
Multilingual Inkling leads
Inkling: 54.0 (#52), Llama 4 Scout: 41.0 (#212)
| Benchmark | Inkling | Llama 4 Scout |
|---|---|---|
| LMArena Non-English | 1434 | 1252 |
| LMArena Chinese | 1490 | 1255 |
| LMArena French | 1458 | 1282 |
| LMArena German | 1446 | 1272 |
| LMArena Japanese | 1429 | 1206 |
| LMArena Korean | 1404 | 1207 |
| LMArena Russian | 1429 | 1263 |
| LMArena Spanish | 1448 | 1278 |
Instruction Following Inkling leads
Inkling: 75.1 (#71), Llama 4 Scout: 65.8 (#217)
| Benchmark | Inkling | Llama 4 Scout |
|---|---|---|
| LMArena Instruction Following | 1426 | 1248 |
| IFEval | — | 81.8% |
Long Context Inkling leads
Inkling: 43.8 (#86), Llama 4 Scout: 27.5 (#294)
| Benchmark | Inkling | Llama 4 Scout |
|---|---|---|
| LMArena Longer Query | 1434 | 1265 |
| Fiction.LiveBench | — | 36% |
Writing & Preference Inkling leads
Inkling: 65.2 (#51), Llama 4 Scout: 37.0 (#261)
| Benchmark | Inkling | Llama 4 Scout |
|---|---|---|
| LMArena Text | 1441 | 1279 |
| LMArena Creative Writing | 1387 | 1249 |
| EQ-Bench Creative Writing | 1611 | 783 |
| LMArena Multi-Turn | 1436 | 1280 |
| WildBench | — | 78% |
| EQ-Bench 4 | 1226 | — |
Frequently asked questions
Is Inkling better than Llama 4 Scout?
Inkling is the stronger model overall, scoring 44.1 to 27.7 on the Noometry Index. Llama 4 Scout costs 17× 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 4 Scout?
Llama 4 Scout is cheaper. It lists at $0.10 per million input tokens and $0.30 per million output tokens; Inkling lists at $1.87 and $4.68.
Is Inkling or Llama 4 Scout better for coding?
Inkling scores higher on coding benchmarks: 34.5 versus 20.2 in the Noometry coding category.
Which has the bigger context window?
Llama 4 Scout does, with 128K tokens against 66K.
How many benchmarks do Inkling and Llama 4 Scout share?
27 benchmarks have published results for both models. Inkling has 41 scored results on Noometry and Llama 4 Scout has 43.