Substack/Medium draft — v3
Status: v3 — DeepSeek-first recommendation (follows corrected numbers); double-hyphens replaced; URL corrected; date corrected
The conversation around AI coding agents has shifted. It’s not “can they code?” anymore. It’s “can you afford to run them at scale?”
When you’re running a single agent, token costs are a rounding error. When you’re running swarms (multiple agents working in parallel, each burning tokens on reasoning, context, and output), the math changes fast. The model you pick stops being a capability decision and starts being a budget decision.
I ran the numbers on three models that keep coming up in this space: DeepSeek V4-Flash, Qwen 3.7 Max, and Kimi K3. Here’s what they actually cost.
The Full Price Table
Prices per million tokens. Cache hit, fresh input, and output all matter for swarm cost:
| Model | Cache Hit Input | Fresh Input | Output |
|---|---|---|---|
| DeepSeek V4-Flash | $0.0028 | $0.14 | $0.28 |
| Qwen 3.7 Max | $1.25 | $1.25 | $3.75 |
| Kimi K3 | $0.30 | $3.00 | $15.00 |
Sources: DeepSeek V4-Flash pricing at deepseek.ai/pricing; Qwen 3.7 Max pricing at benchlm.ai/alibaba/api-pricing; Kimi K3 pricing at benchlm.ai/moonshot/api-pricing. Verified mid-2026.
What the Numbers Actually Say
DeepSeek wins on price in every column, by a wide margin.
At $0.14/M fresh input, DeepSeek runs at roughly 9x lower input cost than Qwen. At $0.28/M output, it’s 13x cheaper than Qwen’s $3.75/M. With cache hits enabled, the gap widens further: $0.0028/M vs $1.25/M is effectively irrelevant in cost terms.
Kimi K3’s $15/M output cost disqualifies it for most swarm configurations. Coding agents generate significant output (code, reasoning, explanations). At $15/M, a Kimi K3 swarm doing generative work costs more than DeepSeek by an order of magnitude on the output side alone.
The Actual Recommendation: DeepSeek V4-Flash
For most swarm use cases, DeepSeek V4-Flash is the right default.
The cost advantage holds at both cache-hit and fresh-input rates. A swarm that never hits cache is still 9-13x cheaper per token than Qwen. A swarm with good cache architecture is effectively free at scale by comparison.
The caveat is real: cache hits require architecture. DeepSeek’s $0.0028/M cache rate is compelling only when your swarm is referencing stable shared context (the same codebase, the same system prompt, the same reasoning scaffold). If your agents are doing varied, exploratory work with minimal context overlap, you’ll pay the $0.14/M fresh rate. That is still the cheapest option in this comparison: even with zero cache hits, the fresh rate holds a 9x input and 13x output advantage over Qwen. What you lose without cache architecture is the near-free tier, not the lead.
The operational unknown is performance at this price. DeepSeek’s coding quality and instruction-following in complex multi-agent orchestration contexts are operator-reported rather than settled by formal benchmarks at the time of writing. If your workload requires precise, low-iteration code generation with complex tool-use chaining, validate before committing a production swarm.
When Qwen 3.7 Max Makes Sense
Qwen is not competitive on price. It’s competitive on predictability.
There’s no cache-vs-fresh cliff to architect around. The input price is flat at $1.25/M regardless of cache state. For teams that want to budget without modeling cache-hit rates, or for workloads where the performance characteristics of Qwen are already validated and the cost premium is acceptable, Qwen is a defensible choice. It’s just not the cost-optimal one.
The Swarm Design Lesson
Model selection matters less than architecture.
DeepSeek’s economics are best-in-class, but they reward swarm designs built to hit cache: stable shared context, repeatable task patterns, consistent prompt structures. Before you optimize the model, optimize the orchestration layer. Then pick the model that matches the cost profile your design actually produces.
For most teams starting out: DeepSeek V4-Flash. Verify performance on your specific task profile before scaling.
Sources
– DeepSeek V4-Flash pricing: deepseek.ai/pricing
– Qwen 3.7 Max pricing: benchlm.ai/alibaba/api-pricing
– Kimi K3 pricing: benchlm.ai/moonshot/api-pricing
– All prices verified mid-2026. Token prices change; verify current rates before committing to a swarm budget.
Chris Meredith writes about AI, technology, and what it actually means for real people. Follow along on Substack: monkeyattack.substack.com
