Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Qwen3 14B (Reasoning) | Inkling (xhigh) |
|---|---|---|
| Input ($/M tokens) | $0.35 | $1 |
| Output ($/M tokens) | $4.2 | $4.05 |
Verdict. Inkling (xhigh) takes the aggregate benchmark matchup 2–0 across 2 categories. Real workloads usually care about a handful of specific tasks — see the per-benchmark table above.
Pricing. Both models sit in the mid-tier bracket for output-token pricing. At 1.0× the per-million-token cost, Inkling (xhigh) is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Inkling (xhigh) makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. Qwen3 14B (Reasoning) is strongest on Coding Index (13.8), Intelligence Index (10.4). Inkling (xhigh) leads on Coding Index (52.1), Intelligence Index (42.3).
Speed. On throughput, Inkling (xhigh) generates tokens at 74 tok/s versus 59 tok/s — about 21% faster. On time-to-first-token, Inkling (xhigh) responds in 1820ms vs 2690ms, which matters most for chat-style UIs.
Provider. Alibaba and Thinking Machines sell to overlapping but distinct developer audiences: Alibaba tends to ship frontier reasoning models with premium positioning, while Thinking Machines 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): Qwen3 14B (Reasoning) costs $73.50 ($882/year); Inkling (xhigh) costs $90.75 ($1089/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Qwen3 14B (Reasoning) ≈ $10.15/run, Inkling (xhigh) ≈ $13.10/run. At agent/realtime scale (200M input / 100M output per million requests): Qwen3 14B (Reasoning) ≈ $490/run, Inkling (xhigh) ≈ $605/run. Qwen3 14B (Reasoning) 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.
Data from Artificial Analysis API — 12 benchmarks
Qwen3 14B (Reasoning) is cheaper overall. Its blended price (3:1 input/output ratio) is $1.31/M tokens vs $1.76/M for Inkling (xhigh).
Inkling (xhigh) wins 2 out of 12 benchmarks compared to 0 for Qwen3 14B (Reasoning). See the detailed benchmark chart above for per-category results.
Inkling (xhigh) generates tokens faster at 74 tok/s vs 59 tok/s. However, Inkling (xhigh) has lower time-to-first-token (1.82s vs 2.69s).
Choose based on your priorities: Qwen3 14B (Reasoning) for lower cost, Inkling (xhigh) for stronger benchmark performance, and Inkling (xhigh) for faster generation. For latency-sensitive apps, check the TTFT comparison above.