Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Ling-flash-2.0 | Llama 3.1 Instruct 70B |
|---|---|---|
| Input ($/M tokens) | $0.14 | $0.56 |
| Output ($/M tokens) | $0.57 | $0.56 |
Verdict. Ling-flash-2.0 wins the overall benchmark matchup 1–0 across 1 overlapping categories, but raw benchmark score is only one input to the decision.
Pricing. Both models sit in the budget bracket for output-token pricing. At 1.0× the per-million-token cost, Llama 3.1 Instruct 70B is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Llama 3.1 Instruct 70B makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. Ling-flash-2.0 is strongest on Intelligence Index (9.6). Llama 3.1 Instruct 70B leads on Intelligence Index (6.5).
Speed. On throughput, Llama 3.1 Instruct 70B generates tokens at 63 tok/s versus 43 tok/s — about 33% faster. On time-to-first-token, Llama 3.1 Instruct 70B responds in 1520ms vs 2710ms, which matters most for chat-style UIs.
Provider. InclusionAI and Meta sell to overlapping but distinct developer audiences: InclusionAI tends to ship frontier reasoning models with premium positioning, while Meta 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): Ling-flash-2.0 costs $12.75 ($153/year); Llama 3.1 Instruct 70B costs $25.20 ($302/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Ling-flash-2.0 ≈ $1.84/run, Llama 3.1 Instruct 70B ≈ $3.92/run. At agent/realtime scale (200M input / 100M output per million requests): Ling-flash-2.0 ≈ $85/run, Llama 3.1 Instruct 70B ≈ $168/run. Ling-flash-2.0 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
Ling-flash-2.0 is cheaper overall. Its blended price (3:1 input/output ratio) is $0.25/M tokens vs $0.56/M for Llama 3.1 Instruct 70B.
Ling-flash-2.0 wins 1 out of 12 benchmarks compared to 0 for Llama 3.1 Instruct 70B. See the detailed benchmark chart above for per-category results.
Llama 3.1 Instruct 70B generates tokens faster at 63 tok/s vs 43 tok/s. However, Llama 3.1 Instruct 70B has lower time-to-first-token (1.52s vs 2.71s).
Choose based on your priorities: Ling-flash-2.0 for lower cost, Ling-flash-2.0 for stronger benchmark performance, and Llama 3.1 Instruct 70B for faster generation. For latency-sensitive apps, check the TTFT comparison above.