StepFun’s Step-5 Preview listing prices cache reads at $0.05 per million tokens. On the same axis, Meta’s Muse Wonder charges $0.20, and Anthropic’s Claude Opus 5.5 charges $0.20 on its OpenRouter listing. Google’s Gemini 4 Argon enters at $2/$10 per million tokens for its introductory window before moving to $4/$20 on Dec 1, putting its standard cache-read tier in the same $0.20 cluster. That 4x gap between $0.05 and $0.20 per re-read is the structural difference that forces a pricing question: where does the cost of keeping context hot actually sit?
When model performance converges, the pricing tier becomes the competitive axis. The AA Intelligence Index v4.3.2 (fetched Oct 13, 2026) places the top four models within 2 points: Opus 5.5 Max at 58, with Sonnet 5.5 Max, Opus 5.5 Xhigh, and Meta’s Wonder Full Max all at 56. At that compression, the 4x cache-read gap is the clearest cost signal an infrastructure planner has. If the intelligence tier is effectively flat across the cluster, the re-read price is the unit of competition for persistent agents.
The unit economics compound over the lifecycle of a persistent agent. In a scenario involving a 1-million-token cache with 1,000 re-reads per day – the workload shape that dominates the top of Step-5’s own traffic table – the cost difference is measurable. At $0.05 per million tokens, the daily expenditure is $50, totaling $1,500 per month. At $0.20 per million tokens, the cost rises to $200 per day, or $6,000 per month. That $4,500 monthly delta per agent is the gap between a pilot and a production fleet. For enterprises deploying multiple agents across extended agentic AI workflows, the 4x difference is not marginal – it is the arithmetic that decides which architectures scale.
Anthropic’s Haiku 5.5 complicates the picture. For prompts over 100,000 tokens, it prices cache reads at $0.05 per million tokens – matching StepFun’s floor. For prompts at or below 100,000 tokens, the rate drops to $0.01 per million. But Haiku 5.5 occupies a different workload tier: it is positioned as a cost-efficient successor to Haiku 4.5, not a frontier reasoning model competing for complex multi-step agentic tasks. The floor match is real, but the workload alignment is not.
The read side is where the floor sits. The write side is where the risk sits. StepFun publishes no cache-write rate on its OpenRouter listing. Anthropic, by contrast, publishes $5.00 per million tokens for a standard cache write and $8.00 for one-hour storage – both above its $4.00 input price, as detailed in our cache-first economics report. For a deployer modeling the total cost of ownership for a persistent agent, that blank line in StepFun’s pricing is a missing term in the cost model. The read rate is aggressive today; the write terms are absent. OpenRouter’s own pricing page carries a note that names the structural reality: \”Caching and discounts mean the price actually paid is often well below the listed one.\”
The Step-5 Preview listing describes the model as \”designed for extended tasks that span large codebases and documents, using tools and refining results over multiple steps.\” Its sparse Mixture-of-Experts design – 600 billion total parameters with 27 billion active – is engineered for exactly the workload shape that makes the $0.05 read rate load-bearing: one large context, written once, re-read on every step. The model was released Oct 8, 2026 on OpenRouter. Its BF16 open weights are promised for Oct 15, but the license remains unpublished on any StepFun channel, as our Oct 15 analysis has tracked.
What matters for the enterprise deployer is not the list price in isolation. It is the ratio: how many tokens are re-read for every token newly written, and at what rate each side is charged. A workload that writes a large context once and re-reads it thousands of times gets the full benefit of the $0.05 floor. A workload that writes frequently and reads infrequently gets nothing from the discount and pays full list. The pricing tier is the competitive axis; the workload shape is what determines whether the savings land.