GPT 5.5 Chat Latest vs Alibaba Qwen3.8-Max — API Pricing, 150K Cache & Latency Benchmark
Side-by-side unit economics audit comparing GPT 5.5 Chat Latest (gpt-5-5-chat-latest: Input $1/1M, Output $4/1M, Warm Cache Read $0.1/1M) against Alibaba Qwen3.8-Max (qwen/qwen3.8-max: Input $0.8/1M, Output $2.4/1M, Warm Cache Read $0.08/1M).
Activate 4-breakpoint prefix caching on GPT 5.5 Chat Latest to cut input token costs from $1/1M down to $0.1/1M, or route async workloads via Batch API for 50% off.
Lock static system prompt prefixes on Alibaba Qwen3.8-Max to pay $0.08/1M warm cache rates instead of $0.8/1M uncached input, plus eligible startup cloud credits.
In Etuki FinOps telemetry comparing GPT 5.5 Chat Latest (API ID: gpt-5-5-chat-latest) vs Alibaba Qwen3.8-Max (API ID: qwen/qwen3.8-max), Alibaba Qwen3.8-Max offers lower standard uncached input pricing at $0.8/1M tokens, while Alibaba Qwen3.8-Max leads in 10-turn 150K cache-pinned coding economics at $0.2760 per 10 turns (saving $0.0890 over GPT 5.5 Chat Latest). For latency-sensitive interactive agents, Alibaba Qwen3.8-Max delivers 30ms faster P90 time-to-first-token (320ms vs 350ms).
| Specification / FinOps Dimension | GPT 5.5 Chat Latest (gpt-5-5-chat-latest) | Alibaba Qwen3.8-Max (qwen/qwen3.8-max) | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | gpt-5-5-chat-latest | qwen/qwen3.8-max | UMCE 563-Registry Locked |
| 563-Registry & Routing Eligibility | is_known: true | routable: true | is_known: true | routable: true | Both Merkle Verified |
| Standard Uncached Input (/1M tokens) | $1 / 1M | $0.8 / 1M | Alibaba Qwen3.8-Max |
| Warm Prefix Cache Read (/1M tokens) | $0.1 / 1M | $0.08 / 1M | Alibaba Qwen3.8-Max |
| Standard Output (/1M tokens) | $4 / 1M | $2.4 / 1M | Alibaba Qwen3.8-Max |
| Context Window Capacity | 200K Tokens | 200K Tokens | GPT 5.5 Chat Latest |
| 150K Prompt × 10 Turns (Unpinned Cold + 2K Out) | $1.5800 | $1.2480 | Alibaba Qwen3.8-Max |
| 150K × 10 Turns Case A (100% Static Pinned) | $0.3650 (saves $1.2150) | $0.2760 (saves $0.9720) | Alibaba Qwen3.8-Max (-$0.0890) |
| 150K × 10 Turns Case B (90% Static + 10% Tail) | $0.4865 (saves $1.0935) | $0.3732 (saves $0.8748) | Alibaba Qwen3.8-Max |
| P50 / P90 First-Token Latency | 238ms / 350ms (87 tok/s) | 218ms / 320ms (105 tok/s) | Alibaba Qwen3.8-Max (-30ms) |
1. Full-Turn Rate-Card Substitution for GPT 5.5 Chat Latest: At an uncached input rate of $1/1M, output rate of $4/1M, and warm cache read rate of $0.1/1M, running 10 consecutive 150,000-token coding agent turns (with 2,000 output tokens/turn) without prefix pinning costs $1.5800. Locking 100% static prefixes (Case A: 1 cold turn + 9 warm cached turns + 20K total output) reduces 10-turn spend to $0.3650 (saving $1.2150); under a 90% static prefix + 10% dynamic tail workload (Case B), 10-turn spend is $0.4865.
2. Full-Turn Rate-Card Substitution for Alibaba Qwen3.8-Max & Winner Verdict: On Alibaba Qwen3.8-Max ($0.8/1M input, $2.4/1M output, $0.08/1M warm cache), 10 unpinned 150K turns cost $1.2480, dropping to $0.2760 in Case A and $0.3732 in Case B. Across every 10-turn 150K coding loop (including output tokens), Alibaba Qwen3.8-Max delivers an additional $0.0890 Case-A cost advantage.
Production Workload TCO: 25,000 MAU • 50M Tokens/Mo (50% Cache Hit) • 2,000 GB Egress
💡 Optimal FinOps Verdict: Deploying Alibaba Qwen3.8-Max saves $20.40/month ($245/year) at 25,000 MAU & 50M monthly tokens (80% input / 20% output split with 50% prefix cache hit rate).