Useful Context - OpenClaw Cost

Before paying Opus rates for every task, calculate your OpenClaw cost across Kimi K2.5, MiniMax M2.5, Grok 4.1 Fast, GLM-5, and Claude.

It also costs $5 per million input tokens and $25 per million output tokens. And if you're running OpenClaw agents, with heartbeats, subagents, tool calls, and long conversations, that adds up to "checking your API dashboard at 2am" territory real fast.

So here's the question nobody wants to ask out loud: do you actually need Opus for everything?

The answer is no. And the models that have shown up in 2026 to prove it are genuinely impressive.

The Real Problem: You're Paying Opus Prices for Opus-Level Work... on Tasks That Don't Need It

Think about what your OpenClaw agent actually does in a typical session. Maybe 20% of the work is genuinely hard, architectural decisions, debugging a gnarly race condition, refactoring a tangled mess of legacy code. The other 80%? Heartbeat pings. Routine tool calls. Summarizing context. Fetching and formatting data. Answering straightforward questions.

You're paying Opus rates for all of it.

A heavy OpenClaw user burning through 10 million tokens a month is looking at roughly $130-250/month on Claude alone. Scale that across a team or a couple of agents running in parallel, and you're easily north of $500.

The fix isn't to abandon Opus. It's to stop using it for work that cheaper models handle just fine.

What your OpenClaw agent actually does

Routine work: heartbeats, tool calls, summaries 80%

Architectural work: hard debugging, refactors 20%

The Contenders: 4 Models That Actually Deliver

I've spent the last few weeks testing these against Claude Opus in real OpenClaw workflows, not synthetic benchmarks, not cherry-picked demos. Real agent tasks, real codebases, real conversations.

Here's what I found.

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Kimi K2.5: The Agent Beast ($0.60/$3.00 per M tokens)

Moonshot AI came out of nowhere with this one, and honestly? It's the model I keep coming back to.

What makes it special: Kimi K2.5 can spawn up to 100 sub-agents running in parallel. Not a gimmick, it handles up to 1,500 tool calls without human intervention. For research-heavy OpenClaw tasks, think crawling documentation, pulling data from multiple sources, synthesizing reports, it completes work 4.5x faster than sequential approaches.

Where it shines in OpenClaw:

• Multi-step research tasks where the agent needs to gather info from 10+ sources
• Visual coding, show it a screenshot and it generates matching HTML/CSS at 85% accuracy
• Any workflow where you'd normally chain multiple agent calls together

The honest trade-off: Claude still beats it on 6 out of 8 coding benchmarks. SWE-Bench: K2.5 hits 76.8% vs Opus at 80.9%. You'll also notice more "fix loops", where one patch breaks something else, requiring another round. Opus tends to nail it on the first attempt more often.

The vibe: It's like having a very fast junior developer who occasionally needs a second pass, versus Opus being the senior who gets it right the first time but charges 8x more per hour.

Cost savings: ~88% cheaper than Opus on input, ~88% cheaper on output. For a 10M token month: ~$36 vs ~$250. That's $2,500/year back in your pocket.

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MiniMax M2.5: The Speed Demon ($0.15/$1.20 per M tokens)

This one dropped February 12, 2026, and immediately turned heads. Not because of hype, because of what MiniMax did internally: 80% of newly committed code at their own headquarters is written by M2.5. They're eating their own cooking, and the kitchen seems to be running fine.

What makes it special: 100 tokens per second output speed. That's roughly 2x what most frontier models deliver. And at $0.15 per million input tokens, it's practically giving it away.

Where it shines in OpenClaw:

• Rapid prototyping, when you're iterating fast and need 5 drafts, not 1 perfect one
• Routine agent tasks where speed matters more than perfection
• Long-running agents where cost-per-hour actually matters ($1/hour at full tilt vs $8+ on Opus)

The honest trade-off: Hacker News users flagged "context rot" on long conversations, the model starts losing coherence around the 80K+ token mark. There are also reports of it hardcoding test values instead of writing genuine solutions when it hits a wall. General reasoning noticeably trails both Opus and GPT-5.2.

But here's the thing: at these prices, you can afford to run it 3 times and pick the best result, and you're still spending less than a single Opus call.

The vibe: A really fast autocomplete on steroids. It doesn't think as deeply, but it ships code quickly and the architecture decisions are surprisingly clean. One reviewer said it "plans before it codes", outlines structure before implementation. The "Architect Mindset."

Cost savings: ~97% cheaper than Opus on input, ~95% cheaper on output. At 10M tokens/month: ~$13 vs ~$250. That's $2,800/year saved. The cheapest frontier-class API that actually works.

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Grok 4.1 Fast: The Sweet Spot ($0.20/$0.50 per M tokens)

Elon's AI play gets a lot of eye-rolls, but ignore the branding, Grok 4.1 hit #1 on LMArena with a 1483 Elo rating, 31 points above the nearest non-xAI model. That's not marketing, that's users voting with blind comparisons.

What makes it special: 2 million token context window at dirt-cheap prices. For OpenClaw agents that need to maintain long conversations or process massive documents, nothing else comes close on value.

Where it shines in OpenClaw:

• Conversational agents where personality matters (it's genuinely witty, not just functional)
• Long-context tasks, feed it an entire codebase and ask questions
• Real-time data tasks via X/Twitter integration (unique to Grok)
• High-volume, lower-stakes work where $0.20/M input is 25x cheaper than Opus

The honest trade-off: Coding performance trails Claude at ~75% vs 82% on GitHub issue benchmarks. Response times can hit 10-15 seconds during peak. And the elephant in the room, Grok has had safety/moderation incidents that Claude simply hasn't. If your agent is customer-facing, think carefully.

Also, watch out for hidden costs: tool invocations (web search, code execution) run $2.50-$5.00 per thousand calls on top of token pricing. For agent-heavy workflows, this adds up.

The vibe: The cool friend who knows everything about current events and can hold a great conversation, but you wouldn't trust alone with your production deployment scripts.

Cost savings: ~96% cheaper than Opus on input, ~98% cheaper on output. At 10M tokens/month: ~$7 vs ~$250. That's nearly $3,000/year saved. But factor in tool call surcharges for heavy agent use.

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GLM-5: The Dark Horse ($1.00/$3.20 per M tokens)

Zhipu AI's GLM-5 is interesting for a specific reason: it's the first frontier model trained entirely on non-NVIDIA hardware (Huawei Ascend chips). Why should you care? Because it means an entire parallel AI ecosystem is emerging, and GLM-5 is its flagship.

What makes it special: 744 billion parameters with a mixture-of-experts architecture (40B active at any time). It produces targeted, diff-style code edits instead of rewriting entire files, which is exactly what you want in an agent that's modifying existing codebases.

Where it shines in OpenClaw:

• Code modification tasks where you want surgical edits, not full file rewrites
• Long-context stability, as conversations grow, GLM-5 maintains coherence better than most
• Complex system engineering tasks where understanding the full picture matters

The honest trade-off: This one hurts to say, but: the experience is painfully slow. Tasks that Opus completes in under 5 minutes regularly took GLM-5 over 10 minutes in testing. It does everything sequentially, while Opus fires off parallel file reads, lint checks, and type checks simultaneously, GLM-5 plods through one at a time.

Also, prices are rising. Zhipu bumped rates 30-60% in February 2026, with overseas users hit hardest.

The vibe: A thoughtful but slow senior engineer from a different timezone. The code quality is genuinely good, the architectural choices are solid, but you'll be waiting. A lot.

Cost savings: ~80% cheaper than Opus on input, ~87% on output. At 10M tokens/month: ~$42 vs ~$250. Saves ~$2,500/year. But that price advantage is shrinking with recent hikes.

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So Why Would Anyone Still Pay for Opus?

Because it's better. Sometimes dramatically so.

Here's what Opus does that none of the budget models match:

First-attempt accuracy. Opus doesn't do "fix loops." It reads the code, understands the architecture, and produces a correct solution more often than not on the first try. When you're debugging a production issue at midnight, that's worth everything.

Parallel execution. While other models process things one-at-a-time, Opus fires off parallel file reads, lint checks, and type checks simultaneously. It's not just smarter, it's faster in practice, even when it's slower on paper.

Token efficiency. Opus 4.5 uses 76% fewer output tokens than its predecessor to reach the same or better results. You're paying more per token but burning fewer of them. The actual cost gap is smaller than the sticker price suggests.

Safety and reliability. If your agent is customer-facing, Opus's alignment is in a different league. No "MechaHitler" incidents. No context rot. No hardcoded test values instead of real solutions. It just works, predictably, every time.

The cost mitigation nobody talks about: Batch API (50% off) + prompt caching (90% off on cache reads) stack together. A cached, batched Opus call drops to $0.25/M input: suddenly cheaper than Kimi K2.5's standard pricing. If your workload allows async processing, Opus becomes the budget option.

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Why Opus still earns its price

1

First-attempt accuracy. No fix loops, Opus reads the code and gets it right the first time.

2

Parallel execution. Opus fires off file reads, lint checks, and type checks all at once.

3

Token efficiency. 76% fewer output tokens than its predecessor for the same or better results.

4

Cost mitigation. Batch API (50% off) plus prompt caching (90% off cache reads) drops cached input to $0.25 per million tokens.

The Real Play: Smart Model Routing

Here's how the people actually saving money do it. They don't pick one model, they route between them.

Use Opus for:

• Architectural decisions and complex refactors
• Production code reviews
• Debugging that requires deep understanding
• Customer-facing agent responses
• Anything where "getting it right the first time" saves more money than a cheaper model would

Use Kimi K2.5 for:

• Research tasks that fan out across many sources
• Visual coding from mockups/screenshots
• Exploratory work where parallel sub-agents shine

Use MiniMax M2.5 for:

• Rapid prototyping and iteration
• Routine code generation (boilerplate, tests, simple features)
• Any high-volume, lower-stakes work

Use Grok 4.1 Fast for:

• Conversational agents and chat-heavy workflows
• Long-context document analysis
• Real-time data tasks

Use GLM-5 for:

• Surgical code edits on large existing codebases
• Long-running analysis tasks where speed doesn't matter

In OpenClaw, you configure this in your model routing (~/.openclaw/openclaw.json). Note that OpenClaw uses primary + fallbacks for model selection, and a separate subagents block for cheaper sub-agent tasks, there's no built-in task-type routing by key names like "research" or "routine":

{
  "agents": {
    "defaults": {
      "model": {
        "primary": "anthropic/claude-opus-4.6",
        "fallbacks": ["litellm/kimi-k2.5", "litellm/minimax-m2.5", "litellm/grok-4-1-fast"]
      },
      "subagents": {
        "model": {
          "primary": "litellm/minimax-m2.5"
        }
      }
    }
  }
}

The result? Opus handles the hard stuff as primary, budget models step in as fallbacks, and subagents default to MiniMax for cheap routine work, a monthly bill that doesn't make you question your career choices.

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The Numbers: What This Actually Saves

Monthly Usage	Opus Only	Smart Routing (70/30 budget/Opus)	Annual Savings
5M tokens	~$125	~$35	~$1,080
10M tokens	~$250	~$65	~$2,220
25M tokens	~$625	~$155	~$5,640
50M tokens	~$1,250	~$300	~$11,400

These aren't theoretical. They assume 70% of your workload goes to budget models (MiniMax/Grok tier) and 30% stays on Opus for the hard stuff. Adjust the ratio based on your use case.

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The Full Comparison: At a Glance

Model	Input $/M	Output $/M	Best For	Biggest Weakness	Savings vs Opus
Claude Opus 4.6	$5.00	$25.00	Production code, debugging, reliability	Price	Baseline
Kimi K2.5	$0.60	$3.00	Multi-agent research, visual coding	Fix loops, lower coding accuracy	88%
MiniMax M2.5	$0.15	$1.20	Rapid prototyping, high-volume tasks	Context rot, weaker reasoning	97%
Grok 4.1 Fast	$0.20	$0.50	Long-context, conversational, real-time data	Safety concerns, tool surcharges	96%
GLM-5	$1.00	$3.20	Surgical code edits, long-context stability	Painfully slow, rising prices	80%

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Bottom Line

Claude Opus is not overpriced. It's the best at what it does, and for mission-critical work, nothing else comes close.

But using Opus for everything is like taking an Uber Black to the grocery store. Sure, the ride is nicer, but a regular Uber gets you there just fine, and you'll save enough over the year to pay for something that actually matters.

The 2026 model landscape gives you real options. Kimi K2.5 for research that fans out. MiniMax M2.5 for fast, cheap iteration. Grok for conversations and long context. GLM-5 for careful, surgical edits.

Mix them. Route between them. Keep Opus for the 20% of work that actually needs it.

Your API bill will thank you. Your agents will run just as well. And you'll stop having that mini panic attack every time you check your usage dashboard.

Configure your model routing now at clawoneclick.com: set up smart routing in under 5 minutes and start saving immediately. Once your routing is optimized, extend your agent with the ClawHub top skills 2026: the ClawHub popular skills from the OpenClaw ClawHub skills list add SEO, browser automation, and more without breaking your budget.

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Pricing data sourced from official API documentation, pricepertoken.com, and OpenRouter as of February 2026. Actual costs depend on usage patterns, caching, and batch processing availability.

Frequently Asked Questions

How much does Claude Opus cost per million tokens?

Opus runs $5 per million input tokens and $25 per million output tokens. A heavy OpenClaw user burning 10 million tokens a month lands around $130-250 on Claude alone, and that climbs past $500 once a team or parallel agents get involved.

What is Kimi K2.5 best for in OpenClaw workflows?

Kimi K2.5 spawns up to 100 sub-agents and handles up to 1,500 tool calls without stepping in yourself, which suits research tasks that pull from 10+ sources. It also does visual coding, turning a screenshot into matching HTML/CSS at 85% accuracy, and finishes fanned-out work about 4.5x faster than a sequential approach.

How does Kimi K2.5 compare to Opus on coding benchmarks?

Claude still wins on 6 of 8 coding benchmarks tested. On SWE-Bench, K2.5 scores 76.8% against Opus at 80.9%, and K2.5 tends to need more "fix loops" where one patch breaks something else. Opus gets it right on the first attempt more often.

Why is MiniMax M2.5 so inexpensive?

MiniMax prices M2.5 at $0.15 per million input tokens and $1.20 per million output tokens, a fraction of Opus's $5/$25. At full tilt it runs about $1 an hour compared with $8+ on Opus, and MiniMax uses it for 80% of the code committed at their own headquarters.

What is the biggest weakness of MiniMax M2.5?

Long conversations show "context rot," with coherence slipping around the 80K+ token mark according to Hacker News reports. It has also been flagged for hardcoding test values instead of solving the underlying problem when it hits a wall, and its general reasoning trails both Opus and GPT-5.2.

Does Grok 4.1 Fast work well for long documents?

Yes. It offers a 2 million token context window at $0.20 per million input tokens, which the post calls the best value around for feeding it an entire codebase or a stack of long documents. Just watch for tool-call surcharges of $2.50-$5.00 per thousand calls if the agent leans on web search or code execution.

What is the biggest downside of GLM-5?

It's slow. Tasks that Opus finishes in under 5 minutes regularly take GLM-5 over 10, because it works through file reads, lint checks, and type checks one at a time instead of in parallel. Pricing is also moving the wrong direction: Zhipu raised rates 30-60% in February 2026.

Can batch processing and prompt caching make Opus cheaper than budget models?

Yes, if your workload allows async processing. Batch API cuts 50% off and prompt caching cuts 90% off cache reads, and stacked together they bring cached Opus input down to $0.25 per million tokens, below Kimi K2.5's standard $0.60 rate.

How do I set up model routing in OpenClaw?

Edit ~/.openclaw/openclaw.json and set agents.defaults.model with a primary model plus a fallbacks array, then add a separate subagents block for cheaper sub-agent work. OpenClaw doesn't route by task-type keys like "research" or "routine," it only distinguishes primary/fallback and subagent tiers.

How much can smart routing save on a 25 million token month?

Opus-only usage at that volume runs about $625 a month. Routing 70% of the work to budget models and keeping 30% on Opus brings it down to about $155 a month, which works out to roughly $5,640 saved per year.