Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Granite 4.0 H Small | Ling 3.0 Flash |
|---|---|---|
| Input ($/M tokens) | $0.06 | $0.07 |
| Output ($/M tokens) | $0.25 | $0.22 |
Verdict. Ling 3.0 Flash 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 budget bracket for output-token pricing. At 1.1× the per-million-token cost, Ling 3.0 Flash is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Ling 3.0 Flash makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. Granite 4.0 H Small is strongest on Intelligence Index (6.0). Ling 3.0 Flash leads on Coding Index (50.6), Intelligence Index (20.6).
Speed. On throughput, Ling 3.0 Flash generates tokens at 323 tok/s versus 15 tok/s — about 95% faster. On time-to-first-token, Ling 3.0 Flash responds in 2780ms vs 29590ms, which matters most for chat-style UIs.
Provider. IBM and InclusionAI sell to overlapping but distinct developer audiences: IBM tends to ship frontier reasoning models with premium positioning, while InclusionAI 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): Granite 4.0 H Small costs $5.55 ($67/year); Ling 3.0 Flash costs $5.40 ($65/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Granite 4.0 H Small ≈ $0.80/run, Ling 3.0 Flash ≈ $0.79/run. At agent/realtime scale (200M input / 100M output per million requests): Granite 4.0 H Small ≈ $37/run, Ling 3.0 Flash ≈ $36/run. Ling 3.0 Flash 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
Both models have similar pricing. Check the detailed breakdown above for input vs output token costs.
Ling 3.0 Flash wins 2 out of 12 benchmarks compared to 0 for Granite 4.0 H Small. See the detailed benchmark chart above for per-category results.
Ling 3.0 Flash generates tokens faster at 323 tok/s vs 15 tok/s. However, Ling 3.0 Flash has lower time-to-first-token (2.78s vs 29.59s).
Choose based on your priorities: both are similarly priced, Ling 3.0 Flash for stronger benchmark performance, and Ling 3.0 Flash for faster generation. For latency-sensitive apps, check the TTFT comparison above.