Cheapest AI Models for Agents in 2026: Full Price Comparison

GPT-6 Luna costs $0.50 per million output tokens. GPT-5.5 still costs $30. Here is what that gap means when your agent runs all month.

The agent ran for eleven days before anyone checked the invoice. Six hundred and forty dollars, for a support bot answering maybe two hundred questions a day.

Nothing was broken. The model was just expensive, the system prompt was long, and no one had noticed that resending the same 12,000 tokens of tool definitions on every single turn was the entire bill.

But price per token is not price per task, and the gap between those two numbers is where most agent budgets die.

All prices below were re-checked on the providers' own pricing pages on 12 November 2026. This category reprices constantly. Since our August version, DeepSeek retired V4-Flash and moved to peak and off-peak pricing, Moonshot retired Kimi K2.5, Zhipu shipped GLM-5.3, Anthropic and OpenAI both launched $2 / $10 models, and on October 7 Anthropic released Claude Haiku 5.5.

Cheapest AI model in 2026: Kimi, MiniMax, GLM and DeepSeek price comparison

Prices per 1M tokens, November 2026.

Checked: 12 Nov 2026, on each provider's own pricing page. The BetterClaw row is not a model: it is what BetterClaw adds when you bring your own key, which is nothing. With BYOK, model usage is billed by your provider at the rates above and never draws BetterClaw credits.

If you use Kimi, note the K2 rows: since 31 October 2026 Moonshot no longer offers the K2 series (including K2.6 and K2.7 Code) to newly registered users, and it plans to retire all K2 models by the end of December 2026. Its migration target is Kimi K3.1 Pro at $2.00 / $10.00.

One caveat on Haiku 5.5: its tokenizer counts more tokens for the same text. Anthropic says roughly 30% more than Haiku 4.5, and Simon Willison measured about 1.25x, so budget for that on top of the per-token rate.

Three things worth knowing before you read another row.

DeepSeek now has a clock. Peak hours are 01:00 to 04:00 and 06:00 to 10:00 UTC on weekdays, and everything outside them, including weekends, bills at half price. A batch job you can schedule overnight UTC costs half what the same job costs at 9am UTC. The old deepseek-v4-flash model name still works, but it now routes to V4.1 Flash and bills at the new rates.

OpenAI charges more for long prompts. On the GPT-6 family, a request with more than 272K input tokens bills at double the input rate and 1.5x the output rate for the whole request. GPT-6.1 Sol becomes $4 / $15 above that line. Anthropic bills Sonnet 5.5's full 1M context at the standard rate.

Batch is still the easiest discount. Anthropic and OpenAI both take 50% off batch jobs, which puts Sonnet 5.5 and GPT-6.1 Sol at $1 / $5. Moonshot charges 60% of the standard rate for batch on K2.7 Code and K2.6, and K3 and K3.1 Pro are on its batch list too.

Cheapest model for OpenClaw in 2026 (Kimi, MiniMax, GLM, DeepSeek)

If you run these models inside OpenClaw, the cheap tier splits in two. Claude Haiku 5.5, Qwen3.8 Flash, GLM-5.3 Flash, DeepSeek V4.1 Flash and MiniMax M3 all handle tool calls, so any of them can be your primary model. GPT-6 Luna is the cheapest per token, but its tool-calling quality inside OpenClaw isn't confirmed yet, so it is a safer fit for the utility model. Kimi K2.7 Code costs $0.95 / $4.00, several times the rows below. Set the main model with agents.defaults.model.primary, list backups in agents.defaults.model.fallbacks, and pick the cheaper side model with agents.defaults.utilityModel. If you'd rather skip config files entirely, see our OpenClaw alternative.

A minimal config that uses a tool-capable cheap model as primary, two fallbacks, and Luna for utility work:

{
  agents: {
    defaults: {
      model: {
        primary: "zai/glm-5.3-flash",
        fallbacks: ["qwen/qwen3.8-flash", "anthropic/claude-haiku-5-5"],
      },
      utilityModel: "openai/gpt-6-luna",
    },
  },
}

Particle-1 vs DeepSeek V4.1 Flash in OpenClaw

Particle-1 is the newest entry in the cheap tier. Per the OpenClaw Particle provider docs, it is served by Particle through an OpenAI-compatible API, takes text and image input, supports reasoning and tool calling, and has a 1,048,576-token context with up to 131,072 output tokens. The catalog price is $0.25 input, $0.03 cached input and $1.00 output per million tokens.

On list price it sits just under DeepSeek V4.1 Flash at peak hours ($0.30 / $1.20) and above it off-peak ($0.15 / $0.60). Two details change the real bill. Particle-1 defaults to high reasoning, and reasoning tokens bill as output, so a task can cost more than the table suggests. DeepSeek halves its price outside 01:00 to 04:00 and 06:00 to 10:00 UTC on weekdays. Run the same five tasks through both before you pick one; cost per finished task matters more than price per token.

Setup in OpenClaw (the provider plugin needs 2026.10.10 or later):

openclaw plugins install @openclaw/particle-provider
openclaw onboard --auth-choice particle-api-key
openclaw models set particle/particle-ai/particle-1

The key goes in PARTICLE_API_KEY. The OpenClaw integration does not support Particle's WebSocket API, file uploads or hosted web search.

Sonnet 5.5 vs GPT-6.1 Sol: the new $2 / $10 tier

The biggest change since August isn't at the cheap end. It's that the tier above it got a lot cheaper.

Anthropic released Claude Sonnet 5.5 on September 28 at $2 per million input tokens and $10 per million output, the same price as Sonnet 5. OpenAI released GPT-6.1 Sol on September 29 at exactly the same $2 / $10. In August, the strongest models we covered cost $25 to $30 per million output tokens. A model in this tier now costs a third of that.

The two tie on list price. They differ in the details that change the real bill:

• Caching. Both now charge $0.10 per million for cached input, so a long, stable system prompt costs the same to resend on either.
• Long context. Sonnet 5.5 bills its whole 1M window at $2 / $10. Sol doubles input and raises output to $15 once a request passes 272K input tokens. For agents that carry big repositories or long document sets, Sonnet comes out cheaper.
• Cache writes. Both charge $2.50 per million to write to the cache.

Neither vendor publishes a SWE-bench Verified score for these models. Anthropic reports 81.3% on SWE-bench Pro for Sonnet 5.5 in its system card. OpenAI reports agentic coding and computer-use scores for Sol on different benchmarks. Since they aren't measured on the same test, test them on your own tool definitions before you pick one.

Opus 5.5 sits one step up at $4 / $20, also cheaper than the Opus it replaced. One catch applies to every recent Claude model: Anthropic's newer tokenizer produces roughly 30% more tokens for the same text, so compare per-task cost, not just the per-token rate.

What agent workloads actually consume

Here is where the standard pricing article stops being useful.

Chat workloads are input-heavy. You send a question, you get a paragraph. Agent workloads are the opposite shape. An agent reasons before it acts, emits tool calls, reads the results, reasons again, and repeats. Every one of those loops produces output tokens.

Output is what bills you. Sort the table by input price and the cheap end looks crowded. Sort by output and the spread runs from $0.47 to $30, more than 60x.

Then there is the cost that appears on no pricing page at all: thinking tokens. A model in reasoning mode can produce a thousand tokens of internal deliberation before a single token of answer. That is why Moonshot cutting thinking tokens by roughly 30% on K2.7 Code versus K2.6, at identical list prices, is a genuine price cut. And why a cheaper per-token model that is more verbose can lose to a pricier one that is terse.

Cost per token is a spec. Cost per completed task is the number you actually pay.

The lever almost no one pulls is caching. Your agent resends the same system prompt and the same tool definitions on every single turn. DeepSeek caches prompt prefixes automatically, and cached input on V4.1 Flash costs $0.006 per million at peak against $0.30 on a cache miss. That is 50 times cheaper on the part of your prompt that never changes. GPT-6.1 Sol's $0.10 against $2 is a 20x cut, and so is Sonnet 5.5's $0.10 against $2 once you count only reads.

Moonshot publishes cache-hit rates too: $0.19 per million on K2.7 Code against $0.95 standard. Same idea, same size of win. Our writeup on prompt caching for agent cost savings covers how to structure prompts so the cacheable part actually stays stable.

What three real agents cost per month

Assumptions stated so you can argue with them: no caching applied unless noted, standard real-time rates, DeepSeek at peak rates, and token counts that include tool definitions and reasoning.

The personal daily driver. 30 tasks a day, roughly 15,000 input and 2,000 output tokens each. That is 13.5M input and 1.8M output a month.

• GPT-6 Luna: about $2.25
• Qwen3.8 Flash: about $2.90
• DeepSeek V4.1 Flash: about $6.20, or $3.10 off-peak
• Kimi K2.7 Code: about $20
• Claude Sonnet 5.5 or GPT-6.1 Sol: about $45
• Claude Opus 5.5: about $90

The small team support agent. 5,000 tasks a month at 8,000 input and 1,500 output each. 40M input, 7.5M output.

• GPT-6 Luna: about $7.75
• DeepSeek V4.1 Flash: about $21, or $10.50 off-peak
• MiniMax M3: about $21
• Kimi K2.7 Code: about $68
• Claude Haiku 5.5: about $7.75, before its tokenizer's extra tokens
• Claude Haiku 4.5: about $78
• Claude Sonnet 5.5 or GPT-6.1 Sol: about $155
• GPT-5.5: about $425

The production coding agent. 500 runs a month, 200,000 input and 40,000 output tokens each, because repo context is enormous. 100M input, 20M output.

• DeepSeek V4.1 Flash: about $54, or $27 off-peak
• Kimi K2.7 Code: about $175, or about $105 on batch rates
• GLM-5.3: about $228
• Claude Sonnet 5.5: about $400
• GPT-6.1 Sol: about $400 at short context. At 200K input per run these requests stay under the 272K line. Push them past it and the same month costs about $700.
• Claude Opus 5.5: about $800

Look at the third scenario again. The gap between the cheapest and most expensive option is still more than an order of magnitude on identical work. What changed since August is the middle. A month of Sonnet 5.5 or GPT-6.1 Sol now costs less than half of what the same month cost on August's top models.

Quality per dollar, where the interesting part is

Cheap is easy. Cheap and good enough is the actual question.

In August, the finding that should change how you shop was a cluster: five models, DeepSeek V4 Pro Max, Gemini 3.1 Pro, MiniMax M3, Qwen3.7 Max and Kimi K2.6, sat between 80.2% and 80.6% on SWE-bench Verified while their output prices ran from $2.40 to $12. Same measured capability, 5x price spread. The top of the field then charged $25 to $30 per million output for roughly eight more points.

That top-tier price step has mostly gone. Sonnet 5.5 and GPT-6.1 Sol cost $10 per million output, and Opus 5.5 costs $20. The vendors have also moved their headline coding numbers to newer benchmarks such as SWE-bench Pro, so you can no longer line the new models up against the August cluster on one chart. Do not plot scores from different benchmarks, or different versions of the same index, on the same axis.

The practical takeaway hasn't changed. Whether a stronger model is worth it depends entirely on whether your tasks live in the gap. For a support agent classifying tickets, they do not. For an agent refactoring production code unsupervised, they might be the whole ballgame. The difference is that trying the strong model on the hard tasks now costs about a third of what it did.

Quick note on where we fit, since this is our blog. We are bring-your-own-key across 28+ model providers with zero markup on inference, which means we make exactly nothing whether you pick the $0.50 model or the $30 one. Our free AI agent plan is one agent with no credit card, and you paste your own DeepSeek, OpenAI or Anthropic key into it. That is the whole reason this article can be honest about which model is cheapest.

Which model to pick by budget

Under $10 a month. GPT-6 Luna, Qwen3.8 Flash or GLM-5.3 Flash for simple, high-volume steps like classification, extraction and summaries. DeepSeek V4.1 Flash costs a little more but has a long production track record for agent work, and if you can schedule jobs off-peak it lands in the same range. Switch caching on and trim your system prompt first. If the budget is zero, our OpenClaw free models list covers the free API tiers and local models that still handle tool calls.

Under $50 a month with a million-token context. MiniMax M3 at $0.30 and $1.20, or DeepSeek V4.1 Flash at the same peak rate. Note MiniMax's rates double above 512K input, and its downloadable weights carry a commercially restricted custom license, which matters if you plan to self-host later.

Under $100 and your workload is MCP-heavy. Kimi K2.7 Code, if your Moonshot account already has it. It led tool-use benchmarks at 76.0 on MCP Atlas and 81.1 on MCP Mark Verified when it launched, and the reduced thinking-token count makes long agent runs cheaper than the list price suggests. New Moonshot accounts can't use it: the K2 series closed to newly registered users on 31 October 2026 and is due to retire by the end of December.

You want a top model without a top-model bill. Claude Sonnet 5.5 or GPT-6.1 Sol at $2 and $10. Pick Sonnet if your prompts run past 272K tokens. On cached, repeated prefixes they now cost the same ($0.10 per million), so test both on your own tool calls.

You want the strongest open weights. GLM-5.3 at $1.40 and $4.40, the same price as GLM 5.2 before it, with a 1M context. It is described as more token-intensive, so budget above the raw rate.

Do not pick one model. Route. Classification and summarization go to a sub-$1 model. Code review and multi-step work go to the $2 / $10 tier. The one task a month that needs Opus goes to Opus. Our guide to model routing to reduce AI costs has the routing rules, and if you are running a self-hosted stack the cheapest provider options for OpenClaw covers the same ground from the framework side.

Worth separating two bills that people merge. Everything above is model spend. The platform running the agent is a second line entirely, and on a hyperscaler it fragments across a dozen meters — our AgentCore pricing breakdown shows how that side adds up.

The cheapest agent stack: local model, budget API, premium tier

Routing goes one step further if you own a decent machine. The cheapest production setup we know has three layers, and the bottom one costs nothing per token.

Layer 1: a local model through Ollama, $0 per token. On a 24GB GPU or a 24GB+ Mac, qwen3.8:27b is an 18GB download with tool calling. On a 16GB machine, qwen3.5:9b is 6.6GB. This layer takes the bulk of the steps: classification, routing, simple extraction, summaries, intent detection. Anything with structured input, structured output and some tolerance for the odd mistake. Turn thinking off for these short turns, and confirm ollama show lists tools before you wire the model into an agent.

Layer 2: a sub-$1 API. GPT-6 Luna, Qwen3.8 Flash or DeepSeek V4.1 Flash, for the jobs that need longer context than your local model gives you, or that must run while your laptop is asleep.

Layer 3: the $2 / $10 tier. Sonnet 5.5 or GPT-6.1 Sol for the small share of work with long tool chains or output a customer will read.

Here is how five everyday personal agents split across those layers:

Be honest about what "production-ready" means at this price. These agents run daily, handle structured work and cost less than a coffee subscription. You do not get an uptime guarantee, automatic failover or audit logs. The local model stops when the machine sleeps, a cheap model will occasionally fumble a tool call, and the 6 PM digest may land at 6:03. That is fine for personal and internal agents. For customer-facing work at scale, move those jobs up a layer and add monitoring.

How to switch between them without rebuilding anything

The good news is that this has become boring, which is the highest compliment available in infrastructure.

Almost every model on this page exposes an OpenAI-compatible endpoint. Switching providers is usually a base URL, an API key, and a model string. That is it. No SDK migration, no rewrite.

Three things that actually bite when you switch. Context windows differ, so a prompt that fits in MiniMax M3's million tokens will not fit Kimi K2.6's 262K, and pricing can change above a threshold even when the prompt fits, as with GPT-6.1 Sol at 272K and MiniMax M3 at 512K. Tool-calling reliability differs more than benchmarks suggest, so test your actual tool definitions rather than trusting a score. And reasoning modes are not standardized, so a model that behaves at default settings may become expensive and slow at high reasoning effort.

On BetterClaw, switching is a dropdown. We support 28+ model providers with bring-your-own-key and no inference markup, so you pay DeepSeek or OpenAI or Anthropic directly at their published rate and we take nothing from it. Point one agent at a cheap model for volume work and another at Sonnet 5.5 or GPT-6.1 Sol for the hard tasks, with per-agent cost caps so a runaway loop cannot produce the $640 invoice from the top of this article. The provider setup docs cover the whole list.

If any of this resonated, start free with BetterClaw. One agent, no credit card, your own API key. Pro is $49 a month for five agents, or $39 on annual, with a 7-day free trial on your first workspace. Full pricing is here. We charge for the platform, never for your tokens.

One closing thought worth more than any row in the table above. Two years ago the cheap models were bad and the good models were expensive, and picking was easy because you had no choice. That is over. The cheapest agent-capable APIs now cost under $0.50 per million output tokens, and the strong mid tier fell to $10. The skill that matters now is not finding the cheapest model. It is knowing which of your tasks actually needs a good one. Most teams have never asked. For a wider view of how rates are moving across every provider, our 2026 LLM pricing guide tracks the whole field.

Frequently Asked Questions

What is the cheapest AI model for agents in 2026?

On list price, GPT-6 Luna ($0.10 input, $0.50 output per million tokens), Qwen3.8 Flash ($0.15 / $0.47) and GLM-5.3 Flash ($0.15 / $0.50) are the cheapest agent-capable APIs as of November 2026. DeepSeek V4.1 Flash is $0.30 / $1.20 at peak hours and half that off-peak, with cache hits from $0.003. Cheap models suit simple, high-volume steps; test them on your own tool calls before routing harder work to them.

What is the cheapest model for OpenClaw in 2026?

For tool-calling agents, GLM-5.3 Flash or Qwen3.8 Flash at $0.15 per million input tokens, or DeepSeek V4.1 Flash off-peak at $0.15 / $0.60 for batch work. GPT-6 Luna and Claude Haiku 5.5 are cheaper per token at $0.10 / $0.50 for prompts under 100K tokens, but Luna's tool-calling quality in OpenClaw isn't confirmed, so treat it as the cheapest per token rather than the default tool-calling pick. Particle-1 ($0.25 / $1.00) is a newer option with tool calling and a 1M context, but it defaults to high reasoning, which bills as output. Set your choice in agents.defaults.model.primary.

How much do Claude Sonnet 5.5 and GPT-6.1 Sol cost?

Both cost $2 per million input tokens and $10 per million output, and both charge $0.10 for cached input. Sonnet 5.5 bills its full 1M context at the standard rate. GPT-6.1 Sol's requests over 272K input tokens bill at $4 / $15. Batch processing halves both to $1 / $5.

What happened to DeepSeek V4-Flash?

DeepSeek retired it. The deepseek-v4-flash name now routes to V4.1 Flash (deepseek-flash), which launched on September 10. Since August 16, DeepSeek also bills by time of day: $0.30 / $1.20 during weekday peak hours (01:00 to 04:00 and 06:00 to 10:00 UTC) and half that at all other times.

How do I actually reduce my agent's token costs?

Start with prompt caching, because agents resend the same system prompt and tool definitions on every turn and cached input costs a fraction of standard rates. Then trim the system prompt itself, cap reasoning effort where the task does not need it, and route simple tasks to a cheaper model rather than sending everything to your best one. On DeepSeek, schedule batch work off-peak. Those together typically matter more than which model you picked.

Is a cheap model worth it, or do you get what you pay for?

For simple, repetitive steps, cheap models are usually enough, and the price gap is large: the cheapest models on this page cost under $0.50 per million output tokens against $10 for Sonnet 5.5 and GPT-6.1 Sol. For long, unsupervised multi-step work, the stronger tier is now cheap enough that it is worth testing before ruling it out. Route each task to the cheapest model that passes your own tests.

Are Chinese-developed models like DeepSeek and Kimi safe to use for business workloads?

That depends on your data policy rather than the model's quality, and it is a question to answer deliberately rather than by default. If sending data to any given jurisdiction is a problem, several of these models ship open weights you can self-host, though you then trade per-token cost for GPU and operations cost. If it is not a problem, check the license terms specifically, since MiniMax M3's downloadable weights carry a commercially restricted custom license while the GLM line is MIT and Kimi's line is Apache 2.0.