AI Model Cost Comparison 2026: Every Major LLM Priced Per Token

AI model pricing is confusing by design. Input tokens cost different amounts than output tokens. Cached tokens get discounts. Some models charge per image. Batch APIs halve the price but add latency.

This guide breaks down every major model's pricing in one table, then shows you how to pick the right price tier for your workload without overspending.

The Full Pricing Table (July 2026)

Sorted by total cost per 1M tokens (input + output combined):

Quality scores from Artificial Analysis Intelligence Index v4.1. Pricing from provider APIs and OpenRouter (July 2026).

Price Per Quality Point

The real question isn't "which is cheapest?" It's "which gives the most intelligence per dollar?"

Luna and Grok 4.5 sit on the cost-performance sweet spot. You get 85-90% of Sol's intelligence for 20-25% of the price.

How to Estimate Your Monthly AI Cost

Three variables determine your bill:

1. Tasks per month: how many times you call the API
2. Tokens per task: average input + output tokens per call
3. Model choice: the price per million tokens

Formula: (tasks × tokens_per_task × price_per_token) = monthly cost

Example: 200 coding tasks/month × 45,000 tokens each × GPT-5.6 Luna pricing:

• Input: 200 × 30,000 × $1.00/1M = $6.00
• Output: 200 × 15,000 × $6.00/1M = $18.00
• Total: $24/month

The same workload on Sol: $6 + $90 = $96/month. On Fable 5: $60 + $150 = $210/month.

Use our Cost Calculator to run these numbers for your specific workload.

The Tiered Model Approach

The smartest teams don't use one model for everything. They route tasks by complexity:

Tier 1: Hard problems (10-20% of tasks)

• Model: GPT-5.6 Sol or Claude Fable 5
• Use for: Architecture decisions, complex debugging, novel problems
• Cost: High per task, but low volume keeps the bill manageable

Tier 2: Standard work (50-60% of tasks)

• Model: Claude Sonnet 5 or Grok 4.5
• Use for: Feature implementation, code review, writing, daily coding
• Cost: Moderate per task, handles the bulk of work

Tier 3: Simple tasks (20-30% of tasks)

• Model: GPT-5.6 Luna or DeepSeek V4 Flash
• Use for: Boilerplate, simple edits, email drafts, summaries
• Cost: Minimal per task, high volume doesn't matter

Result: 60-80% cost reduction vs using a premium model for everything, with minimal quality loss.

Hidden Costs to Watch

Cache Hit Discounts

Some providers discount cached input tokens:

• Kimi K3: $0.30/1M on cache hit (vs $3.00 cache miss). That's a 90% discount.
• Anthropic: Prompt caching can reduce input costs 80-90%
• OpenAI: Repeated system prompts get cached automatically with similar savings

If your workload has repetitive system prompts (agents, batch processing), effective costs can be 50-80% lower than listed prices.

Batch API Pricing

OpenAI's Batch API gives 50% off but responses come within 24 hours. Good for:

• Bulk content generation
• Data processing pipelines
• Non-time-sensitive analysis

Not good for: Real-time interactions, coding agents, anything requiring low latency.

Token Efficiency

Some models use more tokens to accomplish the same task:

• Grok 4.5: Uses 60% fewer tokens per coding task than competitors
• Fable 5: Uses 7.2M tokens per coding agent task vs Sol's 2.6M

A cheaper-per-token model that uses 3x more tokens ends up costing more. Our benchmark dashboard shows token efficiency per model.

Free AI Models Worth Using

Free models are viable for personal projects and experimentation. For production workloads, budget models like DeepSeek V4 Flash ($0.42/1M total) are cheap enough to be effectively free at moderate volume.

When to Upgrade vs Stay Cheap

Stay on budget models when:

• Tasks are repetitive and well-defined
• Quality difference between models isn't noticeable for your use case
• Volume is high (1000+ tasks/day)
• You're building prototypes or MVPs

Upgrade to premium when:

• Error rate on budget models is costing you rework time
• Tasks require reasoning across large contexts
• Output quality directly affects revenue (customer-facing content)
• You're debugging complex, unfamiliar codebases

Summary

The cheapest model that meets your quality bar is the right model. For most developers: GPT-5.6 Luna ($7/1M total) handles 70% of tasks. Claude Sonnet 5 ($12/1M) handles the rest. Monthly cost for moderate usage: $20-50.

For teams processing high volume: DeepSeek V4 Flash ($0.42/1M) for simple work, Grok 4.5 ($8/1M) for complex work. Monthly cost at scale: under $100 for most workloads.

Compare all models live in our LLM Leaderboard or AI Model Comparison Chart. For personalized recommendations, try the Model Selector. And for the full "which model is best" breakdown, see our Best LLM in 2026 guide.