Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Ling 3.0 Tiny | JT-35B-Flash |
|---|---|---|
| Input ($/M tokens) | $0 | — |
| Output ($/M tokens) | $0 | — |
Verdict. Ling 3.0 Tiny and JT-35B-Flash split the benchmark comparison evenly at 1–1. The tiebreaker here is price, speed, and what you actually run them on.
Pricing. Pricing varies significantly between these models — check the table above for the exact per-token rates. Many production workloads actually surface input-token cost (retrieval-augmented prompts, code-context windows), so factor both directions.
Strengths. Ling 3.0 Tiny is strongest on Coding Index (26.5), Intelligence Index (24.5). JT-35B-Flash leads on Intelligence Index (29.0).
Speed. Speed data is incomplete for this pair; benchmark and price should decide.
Provider. InclusionAI and China Mobile sell to overlapping but distinct developer audiences: InclusionAI tends to ship frontier reasoning models with premium positioning, while China Mobile often prices more aggressively. Your existing vendor relationships, billing, and SLA preferences may matter as much as the raw numbers above.
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
Ling 3.0 Tiny is cheaper overall. Its blended price (3:1 input/output ratio) is $0.00/M tokens vs $—/M for JT-35B-Flash.
It's a tie — both models win 1 benchmarks each across 12 evaluated categories. See the detailed benchmark chart above for per-category results.
Ling 3.0 Tiny generates tokens faster at 175 tok/s vs — tok/s. Ling 3.0 Tiny also has lower time-to-first-token (2.57s vs —s).
Choose based on your priorities: Ling 3.0 Tiny for lower cost, both perform similarly on benchmarks, and Ling 3.0 Tiny for faster generation. For latency-sensitive apps, check the TTFT comparison above.