Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Qwen3.7 Max | Jamba 1.5 Large |
|---|---|---|
| Input ($/M tokens) | $2.5 | $2 |
| Output ($/M tokens) | $7.5 | $8 |
Verdict. Qwen3.7 Max wins the overall benchmark matchup 2–0 across 2 overlapping categories, but raw benchmark score is only one input to the decision.
Pricing. Both models sit in the mid-tier bracket for output-token pricing. At 0.9× the per-million-token cost, Qwen3.7 Max is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Qwen3.7 Max makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. Qwen3.7 Max is strongest on Coding Index (66.0), Intelligence Index (46.7). Jamba 1.5 Large leads on Intelligence Index (4.8).
Speed. Speed data is incomplete for this pair; benchmark and price should decide.
Provider. Alibaba and AI21 Labs sell to overlapping but distinct developer audiences: Alibaba 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): Qwen3.7 Max costs $187.50 ($2250/year); Jamba 1.5 Large costs $180.00 ($2160/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Qwen3.7 Max ≈ $27.50/run, Jamba 1.5 Large ≈ $26.00/run. At agent/realtime scale (200M input / 100M output per million requests): Qwen3.7 Max ≈ $1250/run, Jamba 1.5 Large ≈ $1200/run. Jamba 1.5 Large 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 Large is cheaper overall. Its blended price (3:1 input/output ratio) is $3.50/M tokens vs $3.75/M for Qwen3.7 Max.
Qwen3.7 Max wins 2 out of 12 benchmarks compared to 0 for Jamba 1.5 Large. See the detailed benchmark chart above for per-category results.
Qwen3.7 Max generates tokens faster at 212 tok/s vs — tok/s. Qwen3.7 Max also has lower time-to-first-token (2.24s vs —s).
Choose based on your priorities: Jamba 1.5 Large for lower cost, Qwen3.7 Max for stronger benchmark performance, and Qwen3.7 Max for faster generation. For latency-sensitive apps, check the TTFT comparison above.