TL;DR (May 2026): Claude Opus 4.7 is the most accurate but most expensive at $5 / $25 per million input/output tokens. Kimi K2.6 lands close on coding benchmarks at $0.60 / $2.50, roughly one-eighth the cost. DeepSeek V4 Flash is the volume play at $0.14 / $0.28, currently the cheapest tier-1 model on the market. Pick by workload: Opus for review and reasoning, Kimi for everyday coding agents, DeepSeek for high-volume background jobs.
2026 price table
All prices are per million tokens as of May 2026. Numbers come from each provider's public pricing page, linked at the bottom.
| Model | Input | Output | Output / Input ratio | vs Opus 4.7 output |
|---|---|---|---|---|
| Claude Opus 4.7 | $5.00 | $25.00 | 5x | baseline |
| Claude Sonnet 4.6 | $3.00 | $15.00 | 5x | 0.6x |
| Claude Haiku 4.5 | $1.00 | $5.00 | 5x | 0.2x |
| Kimi K2.6 (Moonshot) | $0.60 | $2.50 | 4.2x | 0.10x |
| DeepSeek V4 Pro (75% discount until 2026-05-31) | $0.435 (list $1.74) | $0.87 (list $3.48) | 2x | 0.03x |
| DeepSeek V4 Flash | $0.14 | $0.28 | 2x | 0.011x |
Opus 4.7's output price is roughly 89x DeepSeek V4 Flash and 10x Kimi K2.6. That single ratio drives almost every decision about which model belongs where.
One-day cost example (heavy coding agent run)
Assume an agentic coding workload: 170,000 input tokens (context + tool results + chat) and 30,000 output tokens per developer per day. That's a typical "Claude Code"-style session with large file context, MCP tool calls, and incremental edits.
| Model | Daily cost | Monthly (22 working days) |
|---|---|---|
| Opus 4.7 | $1.60 | $35 |
| Sonnet 4.6 | $0.96 | $21 |
| Kimi K2.6 | $0.18 | $4.00 |
| DeepSeek V4 Pro (discounted) | $0.10 | $2.20 |
| DeepSeek V4 Flash | $0.03 | $0.70 |
For a single developer the absolute numbers are tiny across the board. The math gets interesting when you put 50 engineers on it, or run an autonomous agent that consumes 10x that volume per day per worker. At swarm scale the per-million difference between Opus and Flash is the entire infrastructure budget.
Quality at coding tasks (May 2026 benchmarks)
The price difference would be meaningless if Opus solved problems that Flash cannot. The benchmarks say otherwise on coding-specific tasks.
- SWE-Bench Pro: Opus 4.7 leads at around 91/100; Kimi K2.6 follows at 76.8/100; DeepSeek V4 Pro at 77/100. Kimi closes the gap on practical coding tasks at roughly one-eighth the price.
- Reasoning and knowledge: Opus 4.7 wins clearly. Hidden defects, long-context comprehension, and "is this code actually correct" review tasks favor Anthropic.
- Tool calling reliability: Opus 4.7 and Sonnet 4.6 lead on multi-step agent flows where dropped tool calls compound into wrong outputs.
On pure code generation, the gap between Opus and Kimi is roughly 15 percentage points on SWE-Bench. On reasoning, it's much wider. The question is whether your workload needs the reasoning premium.
Which model for which workload
Interactive developer assistant in an IDE
Opus 4.7 or Sonnet 4.6. The user is sitting there waiting; an extra few seconds of latency from a cheaper model with worse defaults turns into minutes per day. The marginal cost is rounding error against developer salary.
Autonomous coding agent (Claude Code, Aider, Codex CLI in unattended mode)
Sonnet 4.6 for production runs where output quality matters; Kimi K2.6 if your agent does many small iterations and you can verify outputs cheaply. DeepSeek V4 Pro is viable here too, especially while the 75% discount holds. Test on your own task suite before committing.
High-volume background work (bulk summarization, classification, batch RAG)
DeepSeek V4 Flash. The 89x output price gap against Opus makes this a no-brainer for tasks where occasional wrong answers are tolerable (or verifiable cheaply after the fact). If you need slightly better quality, V4 Pro at discount is still 25x cheaper than Sonnet output.
Code review and "did the agent ship a regression"
Opus 4.7. This is the workload that pays for Opus, catching the one subtle bug an autonomous agent missed across thousands of lines of generated code. Use it sparingly and only on the final PR-level pass.
RAG over a private codebase
Use prompt caching. Opus 4.7 cached input drops to $0.50 / million (10% of standard). With a large stable system prompt, Opus can be cheaper than Sonnet for read-heavy patterns.
The hidden cost: tokenizer changes
Opus 4.7 ships with a new tokenizer that produces up to 35% more tokens from the same text. Per-token rates match Opus 4.6, but effective cost per request can be 0 to 35% higher depending on content type. If you migrated from 4.6 to 4.7 and noticed your bill creeping up despite identical traffic, this is why.
Kimi and DeepSeek use their own tokenizers. Cross-provider token counts do not match. The only meaningful comparison is "dollars per business outcome", same task, same eval suite, both providers run end-to-end.
Caching tier matters more than the headline price
All three providers now offer aggressive cached-prompt pricing:
- Anthropic: 10% of standard input rate for cached prompts. Opus cache reads cost $0.50/M vs $5.00/M.
- DeepSeek V4 Flash: cached input drops to $0.0028/M, a 50x reduction from cache-miss.
- DeepSeek V4 Pro: cached input at $0.003625/M (discounted), basically free.
- Kimi K2: context-handling efficiency is implicit in their pricing rather than a separate cache tier.
If your application has a stable system prompt or knowledge base loaded once and queried many times, caching swings the price comparison dramatically. Opus with caching can be cheaper than Sonnet without.
Batch tier: half-price for non-interactive jobs
Anthropic's Batch API processes requests asynchronously within 24 hours at 50% off both input and output. Sonnet 4.6 via batch = $1.50 / $7.50 per million. For nightly classification jobs or evals, batch tier removes the gap with Kimi entirely.
DeepSeek does not currently offer a batch tier; their non-cached price is already the floor of the market.
What we recommend right now
Default stack for a team building AI features in 2026:
- Interactive UI calls → Sonnet 4.6
- Final review / hard reasoning → Opus 4.7 (selective, expensive)
- Background high-volume jobs → DeepSeek V4 Flash
- Coding agents (autonomous loops) → Kimi K2.6 or Sonnet 4.6 with batch tier
The single largest cost mistake we see in usage data is teams using Opus where Sonnet would have been fine, then complaining about the bill. The second largest is using Sonnet where DeepSeek Flash would have been fine. Audit your workload mix quarterly; the savings are not subtle.
How we track this at UsageBox
UsageBox meters AI API calls by model and tokens, then attributes cost to the customer or workload that triggered each call. If you're billing AI features back to end users or rolling AI cost into a SaaS plan, you need this kind of granularity to avoid eating the margin on a noisy customer.
For agent memory across all three providers, our sister project memnode works MCP-side with Opus, Kimi, and DeepSeek alike, same memory layer regardless of which model you route a given request to.
Verified sources (May 2026)
Related on UsageBox
- Gemini API Free Tier (2026), why the free tier vanishes after billing, and the multi-project workaround
- Claude API Billing in 2026, Opus/Sonnet/Haiku rate card, per-minute limits, and stopping $10-in-30-seconds incidents
- GPT-5.5 vs Gemini 3.1 Pro vs Claude Opus 4.7 Pricing, the cross-provider flagship-model cost ladder
- Kimi K2 Billing Explained, Moonshot's pricing, cache tiers, and where Kimi fits in an agent stack
- Cap AI Coding Cost per Engineer, the FinOps playbook for routing spend across these models
- How UsageBox stores all of this: usageDb, the open-source engine that meters per-model token usage, in a 10-part internals series