LLM Cost Comparison 2026: Which Model Actually Costs Less for AI Agents?

The posted price per million tokens tells you very little about what you will actually pay. The number that matters is cost per task, and that depends on token efficiency, retry rate, context overhead, and how much output the model generates to complete each job.

Here is a concrete comparison for the models most commonly used in agent deployments as of May 2026.

Current list prices (May 2026)

Model	Input (per MTok)	Output (per MTok)	Context
Claude Haiku 4.5	$0.80	$4.00	200K
Gemini 2.0 Flash	$0.10	$0.40	1M
Claude Sonnet 4.5	$3.00	$15.00	200K
GPT-4.1	$2.00	$8.00	1M
Llama 4 Scout (Groq)	~$0.11	~$0.34	10M
Claude Opus 4.7	$5.00	$25.00	200K
GPT-5.4	$10.00	$30.00	128K

What agents actually pay per task type

List prices are misleading without knowing how many tokens each task type consumes. Here are representative token counts for common agent tasks, and the resulting per-task cost across models.

Heartbeat check (6,000 tokens input, 200 tokens output)

The most frequent agent call. Loads workspace context, checks for tasks, replies with status or action.

• Gemini 2.0 Flash: $0.0007 per heartbeat
• Claude Haiku 4.5: $0.0057
• GPT-4.1: $0.014
• Claude Sonnet 4.5: $0.021
• Claude Opus 4.7: $0.035

48 heartbeats per day, 30 days, single channel. Monthly cost by model: Gemini Flash $1/month, Haiku $8, GPT-4.1 $20, Sonnet $30, Opus $50. Pick Haiku or Flash for heartbeats. This is not a close call.

Document summarization (20,000 tokens input, 1,000 tokens output)

Reading a long document or codebase and producing a summary. Context-heavy.

• Gemini 2.0 Flash: $0.002 per summarization
• Claude Haiku 4.5: $0.020
• GPT-4.1: $0.048
• Claude Sonnet 4.5: $0.075
• Claude Opus 4.7: $0.125

For bulk document processing where quality is not the bottleneck, Gemini Flash is 60x cheaper than Opus on this task type. The gap is large enough to justify a quality check before committing to a model.

Code review (8,000 tokens input, 2,000 tokens output)

Analyzing a PR or function and producing structured feedback.

• Claude Haiku 4.5: $0.014
• GPT-4.1: $0.032
• Claude Sonnet 4.5: $0.054
• Claude Opus 4.7: $0.090

Code review is where model quality actually matters. A cheap model that misses bugs costs more than it saves. Sonnet is the right default. Use Opus for security-critical review or complex architectural analysis.

Complex reasoning (15,000 tokens input, 3,000 tokens output)

Multi-step planning, architectural decisions, nuanced analysis tasks.

• Claude Sonnet 4.5: $0.090
• GPT-4.1: $0.054
• Claude Opus 4.7: $0.150
• GPT-5.4: $0.240

For complex reasoning, quality differences between models show up concretely. This is where investing in a better model often pays back in fewer retries and better outputs. Test GPT-4.1 and Sonnet before assuming you need Opus or GPT-5.4.

The open-source option

Llama 4 Scout on Groq, DeepSeek V4 Flash on Novita, and similar open-weight models via third-party inference providers run at $0.08-0.25 per million tokens. For heartbeat and classification tasks, these are compelling. For complex reasoning and code review, test thoroughly before committing. Instruction following reliability on adversarial inputs (unusual prompts, edge cases) lags behind proprietary models at the high end.

The right takeaway

There is no single best model for agent workloads. There is a best model for each task type within an agent workload. The difference between running everything on Opus vs. routing by task type is typically 60-85% cost reduction with no meaningful quality loss on the tasks you actually care about.

Run the Clawback calculator with your specific task volumes to see what different routing configurations would cost. It is worth doing once before you commit to a model choice.