Best LLM API for coding agents (2026): priced for real agent workloads

What a coding agent actually needs from an API

A chat user sends a few hundred tokens and reads the answer. A coding agent — Claude Code, OpenCode, ZCode, Aider, Cline, or your own harness — works very differently, and the difference is what makes most API comparisons useless. Five requirements decide whether an API is actually usable for agents:

1. OpenAI-compatible endpoints with real streaming. Every serious agent harness speaks the OpenAI wire format (/v1/chat/completions) and depends on token streaming for cancellation, tool-call parsing, and interactivity. An API that requires a proprietary SDK adds friction at every hop of your stack.

2. Reliable tool calling. Agents act through function calls. The API must emit well-formed tool-call arguments at temperature, and it must do so for long, messy contexts — the benchmark that matters is not a clean single-shot tool-use eval, it's "turn 40 of a messy debugging session with 12 tools defined."

3. Context that fits the job. Repository-scale work means system prompt + file tree + open files + instructions. 32K is tight, 64K is workable with discipline, and 200K–1M changes what you can even attempt — but bigger context multiplies the prefill bill, which brings us to the next two.

4. Prompt caching, priced transparently. Every turn of an agent session re-sends everything before it. Providers that cache prompt prefixes and charge less for the cached portion turn a linear cost curve into a nearly flat one. The catch: many providers don't expose a cached price at all, and some expose the cache but not the price. If you can't see usage.prompt_tokens_details.cached_tokens in the response, you can't reason about your bill.

5. Rate limits that survive a fleet. One agent is one request stream. Ten agents in parallel — or an overnight batch run — hit RPM and concurrent-request ceilings that never appear in a single-user chat. The subscription tiers are especially opaque here: the wall exists, but its exact height is not published, which is why "unlimited" plans and agent fleets have a stormy history.

Requirement #4 is the one that decides the money, so before comparing anyone, let's do the math properly.

The only math that matters: prefill × cache-hit rate

Prefill is the cost of processing your prompt; decode is the cost of generating the answer. Agent sessions are extremely prefill-heavy: a typical turn might carry 40K tokens of context and produce 600 tokens of output. And because an agent re-sends its whole history each turn, most of that prompt is identical to the previous turn — that's a cache hit, and every serious provider prices cache hits at a fraction of fresh input.

Let's model a realistic mid-size session and price it across the field:

• Session shape: 30 turns; each turn's prompt is 40K tokens; 80% of prompt tokens hit the prompt cache (the conversation prefix); each turn emits 600 output tokens.
• Totals per session: 1.2M prompt tokens (960K cached, 240K fresh) and 18K output tokens.

Provider · model	Fresh input $/1M	Cached $/1M	Output $/1M	Modeled 30-turn session
runaii.cloud · GLM-5.3-flash	$0.15	$0.03	$0.50	$0.074
runaii.cloud · DeepSeek V4.1-Flash	$0.22	$0.022	$0.66	$0.086
DeepSeek official · deepseek-flash (peak)	$0.30	$0.006	$1.20	$0.099
DeepSeek official · deepseek-flash (off-peak)	$0.15	$0.003	$0.60	$0.050
runaii.cloud · Qwen3.8-27B Flash	$0.40	—	$1.60	$0.51
Cerebras · Qwen 3.8 27B	see provider	not published	see provider	speed play, not a cost play

Sources: runaii.cloud pricing, DeepSeek API pricing, Cerebras inference models. The worked session numbers are the three cost components added: fresh × price + cached × price + output × price.

Three things jump out of that table. First, per-session costs are cents, not dollars — when the cache works, agent inference is astonishingly cheap. Second, the cached-price column is where the entire ranking lives: Qwen3.8-27B without a published cached price costs ~7× more per session than GLM-5.3-flash on the same workload, purely on the prefill bill. Third, DeepSeek's official cache-hit price ($0.006 peak / $0.003 off-peak) is the cheapest in the field — cheaper than ours — and any honest comparison has to say so out loud.

Now scale it up. Coding-agent power users publish ccusage receipts in the hundreds of millions of tokens per day; a genuinely heavy day for one developer running a small agent fleet looks like 200M+ prompt tokens, >90% cache hits, a few million output tokens. Model a 224M-token day (208M cached / 16M fresh prompt, 8M output):

Provider · model	Cached bill	Fresh bill	Output bill	Heavy day total
runaii.cloud · GLM-5.3-flash	$6.25	$2.36	$4.00	$12.61
runaii.cloud · DeepSeek V4.1-Flash	$4.58	$3.45	$5.28	$13.31
DeepSeek official · off-peak	$0.62	$2.36	$4.80	$7.78
DeepSeek official · peak	$1.25	$4.72	$9.60	$15.57

At that burn, a month of metered inference lands around $230–470 depending on provider and scheduling — which is exactly the price band where the flat subscriptions live. That's the real decision, and it's where the comparison gets interesting.

The 2026 field, provider by provider

runaii.cloud — GLM-5.3-flash from $0.03/1M cached reads

We serve open models — GLM-5.3-flash, DeepSeek V4.1-Flash, Qwen3.8 — behind one OpenAI-compatible API, with the cache split visible in every response (usage.prompt_tokens_details.cached_tokens). GLM-5.3-flash is the agent workhorse: 64K context, vision, a 61.0 SWE-bench Verified-class showing, $0.15/$0.03/$0.50 per 1M tokens. DeepSeek V4.1-Flash adds a 1M-token context window at $0.22/$0.022/$0.66. There's also a per-second GPU tier if you want to run your own weights. For agent fleets specifically, the pitch is boring on purpose: one endpoint, transparent cache math on every model, and Token-Max flat plans (100M–1B tokens/day) if you want the meter to disappear. Where we lose is below, in its own section.

DeepSeek official API — $0.006/1M cache hits, half off off-peak

The official DeepSeek API is the cost benchmark everyone else is measured against: deepseek-flash at $0.30/$0.006/$1.20 peak, and everything halves off-peak — the half-price windows are weekday 04:00–06:00 and 10:00–01:00 UTC plus all weekend (the peak windows, 01:00–04:00 and 06:00–10:00 UTC, are Beijing business hours). If your agent workload can be scheduled — batch runs, overnight CI, evaluation sweeps — off-peak deepseek-flash is the cheapest serious inference in the industry right now. The tradeoffs are the ones you'd expect from a single-lab API: one model family, and the cache prices are great but the interactivity during their peak windows is at the mercy of their demand curve. For interactive agent work during your workday, the calculus gets murkier. We explained how their V4.1-Flash cache gets so cheap in a dedicated teardown — it's an architectural story, not a subsidy.

The GLM lanes — from z.ai's own subscription to $0.07/1M aggregator rates

GLM-5.3 is the open-weight model of the moment — Z.ai published a first-party account of building their own inference stack that became one of the year's most-discussed infrastructure posts, and a month-long hands-on coding review from the Wagtail team held up in practice. Getting it via API is more fragmented than it looks: z.ai's own platform prices GLM-5.3 with a 1M-token context and an off-peak quota discount (50% of standard points), a subscription ladder reported at $18–168/month (as summarized by flowtivity), and third-party aggregators quote GLM-5.3-Flash as low as $0.07/$0.25 per 1M (dgmnews, Sept 2026). Read aggregator rates carefully: the cheap lane is usually the cheap lane — one provider's pool, with its own queueing at peak.

Cerebras — Qwen 3.8 27B at 1,500 tok/s

Cerebras lists Qwen 3.8 27B at ~1,500 tokens/s — an order of magnitude beyond typical GPU serving, on the back of their wafer-scale CS-4 systems. For agent UX, decode speed is real value: tool-call round-trips feel instant, and long generations stop being coffee breaks. But throughput-per-second and cost-per-token are different axes; Cerebras does not publish a cached-read price, so an agent's re-prefill bill is the open question. The right division of labor: Cerebras for the turns where decode latency dominates, a cache-priced provider for the prefill-heavy grind. Some agent harnesses make this per-stage routing a one-line config.

OpenRouter — one endpoint, every model, and a markup

OpenRouter aggregates hundreds of models behind one OpenAI-compatible endpoint, which makes it the fastest way to A/B agents across model families. Two caveats for agent workloads. First, the aggregator margin means you're typically paying somewhat above the underlying provider's direct rate for the convenience. Second — and this is the one that matters for this comparison — their model pages don't surface cached-input pricing, so your agent bill is harder to predict on exactly the axis that dominates it. Aggregators are a discovery tool; once you know which model your fleet runs on, pricing the direct lane (or a cache-transparent provider) against it is due diligence.

The flat subscriptions — Claude Max, Codex, GLM's plans, Token-Max

The subscription tier is having a moment: Anthropic's Claude 5.5 family cut the price umbrella 30–40% across Opus/Sonnet/Haiku, and their Max plans remain the reference experience for Claude Code; OpenAI's Codex went flat-rate ("unlimited 5.6 usage" on Pro, with a $500/mo ProMax tier spotted in API config); z.ai sells GLM subscriptions directly; and Token-Max sits deliberately in the middle: flat monthly, sized for agent burn (100M–1B tokens/day), daily reset, with a meter you can actually count. The honest framing of every flat plan, ours included: you're trading a meter for a ceiling. Subscriptions that hide the ceiling's height create a particular kind of bad week — the one where your agents stop working at 2pm and support can't tell you why. A flat plan with a visible meter (tokens used, tokens left, reset time) gives you the predictability without the surprise.

Where runaii.cloud loses (read this before you buy)

Every provider-side comparison should be legally required to include this section. Three places we genuinely lose:

1. Pure cache-hit price on a single model. DeepSeek's official API charges $0.006–0.003/1M cache hits against our $0.03 (GLM-5.3-flash) and $0.022 (DeepSeek V4.1-Flash). If you run exactly one model, all day, and can shift work off-peak, their official API will beat our metered price and it's not close. We add value on the multi-model, cache-transparent, fleet side — not on beating a single lab's own loss-leader rate.

2. Raw decode speed. Cerebras' wafer-scale throughput is in a different universe from GPU serving. If your product's success metric is time-to-last-token on short generations, route those calls to them.

3. Model breadth. OpenRouter catalogs hundreds of models including frontier closed models; we serve a focused set of open models. If your agents need GPT-class or Claude-class frontier intelligence for a specific task, that's a routing decision, and pretending otherwise would be dishonest.

If your workload matches one of those three, use the winner — we'll still be here for the rest of your routing table.

How to choose by profile

• Solo developer, one agent, evenings-and-weekends: a flat subscription is the least overhead — pick by which harness you already live in (Claude Code → Claude plans; Codex → ChatGPT Pro; budget → GLM's $18 tier or Token-Max at the entry tier).
• Small team, mixed models, cost-conscious: one cache-transparent OpenAI-compatible endpoint beats juggling N provider consoles. Do the session math above on your real harness logs before committing anywhere.
• Overnight/CI batch: DeepSeek's official off-peak rate is the cost floor; target weekday 04:00–06:00 and 10:00–01:00 UTC plus weekends, and pay half.
• Interactive product with decode-latency SLAs: Cerebras for hot turns, a cache-priced lane for the prefill-heavy grind; per-stage routing is a config line in modern harnesses.
• Agent fleet at 100M+ tokens/day: you're in flat-plan territory either way ($230+/day metered is $7K/month); the differentiator stops being price-per-token and becomes whether the ceiling is visible and what happens when you touch it. Read each plan's overflow policy before you sign anything.

The checklist before you commit

Run this against any candidate API with your own harness before you pay a cent:

1. Send a 30-turn session with a large stable prefix; pull usage.prompt_tokens_details.cached_tokens from the response. No cached field? Your agent bill will be 5–10× the chat-user bill for the same tokens.
2. Divide your monthly token forecast by the cached and fresh rates separately. A spreadsheet with one formula beats every blog post's table, including this one — the inputs are our prices, DeepSeek's prices, and your own ccusage logs.
3. Fire 20 parallel tool-calling requests and watch for 429s. Rate-limit behavior under concurrency is where marketing pages go silent.
4. Check the streaming format for tool-call deltas if your harness parses them incrementally.
5. Price the exit: an OpenAI-compatible endpoint means switching providers is a base-URL change. Proprietary SDKs mean switching costs compound quietly forever.

Sources and freshness

• runaii.cloud prices: runaii.cloud/pricing — GLM-5.3-flash $0.15/$0.03/$0.50, DeepSeek V4.1-Flash $0.22/$0.022/$0.66, Qwen3.8-27B Flash $0.40/$1.60 per 1M tokens (2026-10-09).
• DeepSeek official: api-docs.deepseek.com pricing — deepseek-flash peak $0.30/$0.006/$1.20, off-peak half (2026-10-09).
• Cerebras Qwen 3.8 27B throughput: inference-docs.cerebras.ai.
• Z.ai GLM-5.3 context and off-peak quota: docs.z.ai; GLM subscription ladder as summarized by flowtivity; aggregator GLM-5.3-Flash rate quote from dgmnews (Sept 2026).
• Anthropic Claude 5.5 family cost claims: anthropic.com/news; OpenAI Codex flat-rate: chatgpt.com/codex/pricing.
• Agent-workload shape (context re-send per turn, cache-heavy profiles) is common ground across harness docs and the ccusage receipts community; the session/day models above state their assumptions explicitly so you can re-run them with your own numbers.

Prices move weekly in this market. This page carries a changelog; the tables above were last verified 2026-10-09.

Changelog

• 2026-10-09: initial publication. Prices verified against provider pages on this date (runaii.cloud, DeepSeek official, Cerebras listings; z.ai and aggregator rates as cited). Next scheduled re-verification: 2026-11-01, or sooner if a listed provider reprices.