Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Inkling Small | GPT-5.6 Luna (high) |
|---|---|---|
| Input ($/M tokens) | $0.3 | $0.2 |
| Output ($/M tokens) | $1.2 | $1.2 |
Verdict. GPT-5.6 Luna (high) takes the aggregate benchmark matchup 3–1 across 4 categories. Real workloads usually care about a handful of specific tasks — see the per-benchmark table above.
Pricing. Both models sit in the budget bracket for output-token pricing. At 1.0× the per-million-token cost, GPT-5.6 Luna (high) is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). GPT-5.6 Luna (high) makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. Inkling Small is strongest on GPQA Diamond (90%), Coding Index (52.9), SciCode (49%). GPT-5.6 Luna (high) leads on GPQA Diamond (89%), Coding Index (63.3), SciCode (51%).
Speed. On throughput, GPT-5.6 Luna (high) generates tokens at 175 tok/s versus 97 tok/s — about 45% faster. On time-to-first-token, Inkling Small responds in 1688ms vs 5873ms, which matters most for chat-style UIs.
Provider. Thinking Machines and OpenAI sell to overlapping but distinct developer audiences: Thinking Machines tends to ship frontier reasoning models with premium positioning, while OpenAI often prices more aggressively. Your existing vendor relationships, billing, and SLA preferences may matter as much as the raw numbers above.
Workload cost. Workload scenarios (per million requests at 30M input + 15M output tokens): Inkling Small costs $27.00 ($324/year); GPT-5.6 Luna (high) costs $24.00 ($288/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Inkling Small ≈ $3.90/run, GPT-5.6 Luna (high) ≈ $3.40/run. At agent/realtime scale (200M input / 100M output per million requests): Inkling Small ≈ $180/run, GPT-5.6 Luna (high) ≈ $160/run. GPT-5.6 Luna (high) becomes more attractive at higher volume — the absolute per-token pricing difference compounds when you ship at scale.
Recommendation. Both models have legitimate use cases — the right answer depends on whether you are optimizing for benchmark ceiling, latency, or unit cost. Start with the cheaper / faster model, evaluate against your specific task, and only switch if the upgrade shows a meaningful lift.
Head-to-head deltas
Data from Artificial Analysis API — 12 benchmarks
GPT-5.6 Luna (high) is cheaper overall. Its blended price (3:1 input/output ratio) is $0.45/M tokens vs $0.53/M for Inkling Small.
GPT-5.6 Luna (high) wins 3 out of 12 benchmarks compared to 1 for Inkling Small. See the detailed benchmark chart above for per-category results.
GPT-5.6 Luna (high) generates tokens faster at 175 tok/s vs 97 tok/s. Inkling Small also has lower time-to-first-token (1.69s vs 5.87s).
Choose based on your priorities: GPT-5.6 Luna (high) for lower cost, GPT-5.6 Luna (high) for stronger benchmark performance, and GPT-5.6 Luna (high) for faster generation. For latency-sensitive apps, check the TTFT comparison above.