OpenAI GPT-6.1 Sol vs Alibaba Qwen3.8-Max — API Pricing, 150K Cache & Latency Benchmark
Side-by-side unit economics audit comparing OpenAI GPT-6.1 Sol (openai/gpt-6.1-sol: Input $2/1M, Output $10/1M, 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 OpenAI GPT-6.1 Sol to cut input token costs from $2/1M 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 OpenAI GPT-6.1 Sol (API ID: openai/gpt-6.1-sol) 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 $0.3590 over OpenAI GPT-6.1 Sol). For latency-sensitive interactive agents, OpenAI GPT-6.1 Sol delivers 55ms faster P90 time-to-first-token (265ms vs 320ms).
| Specification / FinOps Dimension | OpenAI GPT-6.1 Sol (openai/gpt-6.1-sol) | Alibaba Qwen3.8-Max (qwen/qwen3.8-max) | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | openai/gpt-6.1-sol | 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) | $2 / 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) | $10 / 1M | $2.4 / 1M | Alibaba Qwen3.8-Max |
| Context Window Capacity | 200K Tokens | 200K Tokens | OpenAI GPT-6.1 Sol |
| 150K Prompt × 10 Turns (Unpinned Cold + 2K Out) | $3.2000 | $1.2480 | Alibaba Qwen3.8-Max |
| 150K × 10 Turns Case A (100% Static Pinned) | $0.6350 (saves $2.5650) | $0.2760 (saves $0.9720) | Alibaba Qwen3.8-Max (-$0.3590) |
| 150K × 10 Turns Case B (90% Static + 10% Tail) | $0.8915 (saves $2.3085) | $0.3732 (saves $0.8748) | Alibaba Qwen3.8-Max |
| P50 / P90 First-Token Latency | 180ms / 265ms (115 tok/s) | 218ms / 320ms (105 tok/s) | OpenAI GPT-6.1 Sol (-55ms) |
1. Full-Turn Rate-Card Substitution for OpenAI GPT-6.1 Sol: At an uncached input rate of $2/1M, output rate of $10/1M, 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 $3.2000. Locking 100% static prefixes (Case A: 1 cold turn + 9 warm cached turns + 20K total output) reduces 10-turn spend to $0.6350 (saving $2.5650); under a 90% static prefix + 10% dynamic tail workload (Case B), 10-turn spend is $0.8915.
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 $0.3590 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 $100.40/month ($1205/year) at 25,000 MAU & 50M monthly tokens (80% input / 20% output split with 50% prefix cache hit rate).