Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Llama Nemotron Super 49B v1.5 (Reasoning) | Jamba 1.5 Mini |
|---|---|---|
| Input ($/M tokens) | $0.4 | $0.2 |
| Output ($/M tokens) | $0.4 | $0.4 |
Verdict. Llama Nemotron Super 49B v1.5 (Reasoning) 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, Jamba 1.5 Mini is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Jamba 1.5 Mini makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. Llama Nemotron Super 49B v1.5 (Reasoning) is strongest on Intelligence Index (12.4). Jamba 1.5 Mini leads on Intelligence Index (2.3).
Speed. Speed data is incomplete for this pair; benchmark and price should decide.
Provider. NVIDIA and AI21 Labs sell to overlapping but distinct developer audiences: NVIDIA tends to ship frontier reasoning models with premium positioning, while AI21 Labs 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): Llama Nemotron Super 49B v1.5 (Reasoning) costs $18.00 ($216/year); Jamba 1.5 Mini costs $12.00 ($144/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Llama Nemotron Super 49B v1.5 (Reasoning) ≈ $2.80/run, Jamba 1.5 Mini ≈ $1.80/run. At agent/realtime scale (200M input / 100M output per million requests): Llama Nemotron Super 49B v1.5 (Reasoning) ≈ $120/run, Jamba 1.5 Mini ≈ $80/run. Jamba 1.5 Mini 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
Jamba 1.5 Mini is cheaper overall. Its blended price (3:1 input/output ratio) is $0.25/M tokens vs $0.40/M for Llama Nemotron Super 49B v1.5 (Reasoning).
Llama Nemotron Super 49B v1.5 (Reasoning) wins 1 out of 12 benchmarks compared to 0 for Jamba 1.5 Mini. See the detailed benchmark chart above for per-category results.
Llama Nemotron Super 49B v1.5 (Reasoning) generates tokens faster at 106 tok/s vs — tok/s. Llama Nemotron Super 49B v1.5 (Reasoning) also has lower time-to-first-token (4.23s vs —s).
Choose based on your priorities: Jamba 1.5 Mini for lower cost, Llama Nemotron Super 49B v1.5 (Reasoning) for stronger benchmark performance, and Llama Nemotron Super 49B v1.5 (Reasoning) for faster generation. For latency-sensitive apps, check the TTFT comparison above.