LLM Cost Benchmark 2026: Claude vs GPT-4o vs Mistral vs Groq with Real Use Cases

Your LLM budget is determined by four variables: token pricing, caching hit rate, latency tolerance, and quality requirements per use case. This article gives you complete May 2026 pricing tables for Claude (Opus 4.6, Sonnet 4.6, Haiku 4.5), OpenAI (GPT-4o, GPT-4o mini), Mistral (Large 2, Small 3.1), and Groq (Llama 3.3 70B, Llama 3.1 8B). Plus three real ROI models, cost-per-interaction breakdowns for RAG, agent loops, and vision workloads, and a decision tree you can use today.

1. Complete Pricing Table — May 2026

All prices are public API rates as of May 2026. Batch discount applies to overnight/asynchronous processing queues. Prompt caching prices apply to tokens that were previously processed and stored in the cache (charged on the second+ call, not the first).

Why Caching Changes Everything

Prompt caching lets you reuse repeated input tokens at a fraction of the standard price. Claude's cached token rate ($0.30/1M for Sonnet) is 10× cheaper than standard input — and GPT-4o's cache rate ($1.25/1M) is half its standard rate.

For a RAG application where every query includes a 1,500-token system prompt and a 500-token user profile (2,000 shared tokens), with Claude Sonnet and 90% cache hit rate:

• Uncached: 2,000 × $3.00/1M = $0.0060 per query (input only)
• Cached (90% hit): 200 × $3.00 + 1,800 × $0.30 = $0.0011 per query
• Savings: 82% on input tokens

2. ROI Model 1 — 10,000-User SaaS Chat

Scenario: A B2B SaaS platform with 10,000 active users. Each user sends an average of 40 messages per month to an AI assistant. The assistant has a 1,200-token system prompt (cacheable), retrieves 3 context chunks averaging 300 tokens each (900 tokens, partially cacheable), and generates 250-token responses.

Token math per conversation turn

• System prompt (cached after first call): 1,200 tokens input
• Retrieved context: 900 tokens input
• User message: 80 tokens input
• Response: 250 tokens output
• Total: 2,180 input + 250 output tokens

Monthly volume: 10,000 users × 40 messages = 400,000 requests/month

3. ROI Model 2 — 1M-Request/Month Batch Processing

Scenario: An e-commerce company runs nightly product catalog enrichment. Each request processes one product description (avg 1,800 input tokens) and generates structured JSON output with enhanced description + SEO metadata (avg 600 output tokens). Processing runs between 01:00–06:00 UTC with no latency requirements — ideal for batch API discounts.

Monthly token volume

• 1,000,000 requests × 1,800 input tokens = 1.8B input tokens
• 1,000,000 requests × 600 output tokens = 600M output tokens

Winner for batch: Mistral Small 3.1 at $270/month for 1M nightly requests. For tasks where quality matters more (legal document analysis, nuanced content generation), GPT-4o mini batch ($495) or Mistral Large batch ($6,300) are the next tiers. Claude Sonnet batch at $11,700/month is rarely justified for pure batch workloads unless output quality is the primary constraint.

4. Edge Case — Local Ollama vs Cloud APIs

Self-hosting with Ollama removes per-token billing entirely, replacing it with fixed infrastructure costs. The break-even depends on your daily token volume.

Hardware options and costs (2026)

Break-even analysis

At what daily token volume does self-hosting beat each cloud provider?

• vs. Claude Sonnet 4.6 ($3/$15): A dedicated A100 at $1,800/month breaks even at approximately 190,000 tokens/day (assuming 70/30 input/output split). Above that, Ollama is cheaper.
• vs. Groq Llama 3.3 70B ($0.59/$0.79): Same A100 setup breaks even at approximately 1,100,000 tokens/day. Below that, Groq is more cost-effective.
• vs. Mistral Small 3.1 ($0.10/$0.30): Self-hosting never beats Mistral Small on pure cost unless you generate more than 3M tokens/day — at which point you would use a cluster, not a single machine.

Quickstart: Ollama production setup

# Install Ollama on Linux server
curl -fsSL https://ollama.com/install.sh | sh

# Pull a production-ready model
ollama pull llama3.3:70b           # 70B, best quality
ollama pull mistral:7b-instruct    # 7B, fastest

# Set concurrency limit (matches your GPU VRAM)
export OLLAMA_NUM_PARALLEL=4       # for A100 80GB + Llama 3.3 70B Q4

# Start with OpenAI-compatible API
ollama serve
# → API available at http://localhost:11434/v1

# Test with standard OpenAI client
python3 -c "
from openai import OpenAI
client = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')
response = client.chat.completions.create(
    model='llama3.3:70b',
    messages=[{'role': 'user', 'content': 'Classify this as positive/negative: Great product!'}],
    max_tokens=10
)
print(response.choices[0].message.content)  # → positive
print(f'Tokens used: {response.usage.total_tokens}')
"

5. Cost-per-Interaction by Pattern

The same model can cost 10× more per interaction depending on your architecture. Here are the three most common patterns with realistic token budgets.

RAG Query (retrieval-augmented generation)

Typical token budget: 500-token system prompt + 5 chunks × 400 tokens + 80-token query + 200-token answer = 2,580 input + 200 output tokens

Agent Loop (5-step reasoning + tool use)

A typical 5-step agent loop: 800-token system prompt + 2,000-token accumulated context + 150-token tool results per step, generating 200-token reasoning + tool call per step. Total: ~14,000 input + 1,000 output tokens per loop

• Claude Sonnet 4.6: $0.057/loop — $570 at 10k loops/month
• GPT-4o: $0.045/loop — $450 at 10k loops/month
• Mistral Large 2: $0.034/loop — $340 at 10k loops/month
• Groq Llama 3.3 70B: $0.0093/loop — $93 at 10k loops/month

Vision (image understanding)

Image tokens are charged per tile. A 1024×1024 image typically costs 1,024–1,700 tokens depending on the provider's tiling strategy. For a typical document analysis request (one image + 200-token prompt + 300-token extraction):

• Claude Sonnet 4.6: ~1,500 image tokens + 200 input + 300 output = $0.0110/request
• GPT-4o: ~1,105 image tokens + 200 input + 300 output = $0.0063/request
• GPT-4o mini: same token count = $0.0008/request (best for high-volume vision)
• Mistral: vision available in Mistral Large (pixtral-large) = $0.0085/request
• Groq: no vision support as of May 2026

6. Decision Tree: Choosing Your Provider

START: What is your primary constraint?
├── LATENCY < 200ms required?
│   └── YES → Groq (Llama 3.3 70B: 120ms p50)
│             For very simple tasks: Groq Llama 3.1 8B (80ms)
│
├── DATA SOVEREIGNTY / no cloud?
│   └── YES → Ollama self-hosted
│             Volume > 800k tokens/day? → dedicated server
│             Volume < 800k tokens/day? → local machine (M4 / RTX 4090)
│
├── BATCH processing, latency not important?
│   ├── Quality critical (legal, medical, nuanced)? → Claude Sonnet batch ($1.50 input/1M)
│   ├── Quality moderate (catalog, content)? → GPT-4o mini batch ($0.075 input/1M)
│   └── Cost-first (classification, extraction)? → Mistral Small batch ($0.05 input/1M)
│
└── INTERACTIVE / real-time application?
    ├── Customer-facing, quality critical?
    │   ├── High volume (>500k/month)? → Claude Sonnet + caching
    │   └── Low volume (<100k/month)? → Claude Sonnet or GPT-4o
    │
    ├── Internal tool, quality moderate?
    │   └── Mistral Large or GPT-4o mini (routing by complexity)
    │
    └── Simple tasks (classification, routing, extraction)?
        └── Groq Llama 3.3 70B or Mistral Small
            (10-20× cheaper than Sonnet/GPT-4o for same quality)

7. TCO Calculator — Run This Before You Choose

This Python script calculates your real monthly cost from actual production logs. Run it against 200+ real queries to get an accurate baseline.

#!/usr/bin/env python3
"""
LLM Cost Calculator — May 2026
Calculates monthly LLM spend from sampled production queries.
Usage: python3 llm_cost_calc.py --queries queries.jsonl --volume 400000
"""
import json
import argparse
from pathlib import Path

# May 2026 pricing ($/1M tokens)
PRICING = {
    "claude-sonnet-4-6":   {"input": 3.00, "output": 15.00, "cache": 0.30, "batch_in": 1.50},
    "claude-haiku-4-5":    {"input": 0.80, "output": 4.00,  "cache": 0.08, "batch_in": 0.40},
    "gpt-4o":              {"input": 2.50, "output": 10.00, "cache": 1.25, "batch_in": 1.25},
    "gpt-4o-mini":         {"input": 0.15, "output": 0.60,  "cache": 0.075,"batch_in": 0.075},
    "mistral-large-2":     {"input": 2.00, "output": 6.00,  "cache": None, "batch_in": 1.50},
    "mistral-small-3-1":   {"input": 0.10, "output": 0.30,  "cache": None, "batch_in": 0.05},
    "groq-llama-3-3-70b":  {"input": 0.59, "output": 0.79,  "cache": None, "batch_in": None},
    "groq-llama-3-1-8b":   {"input": 0.05, "output": 0.08,  "cache": None, "batch_in": None},
}

def calculate_cost(queries_file: str, monthly_volume: int, cache_hit_rate: float = 0.0):
    """
    Args:
        queries_file: JSONL file with {"input_tokens": N, "output_tokens": N, "model": "..."} per line
        monthly_volume: expected total requests per month
        cache_hit_rate: fraction of input tokens served from cache (0.0–0.9)
    """
    queries = []
    with open(queries_file) as f:
        for line in f:
            queries.append(json.loads(line.strip()))

    sample_size = len(queries)
    avg_input = sum(q["input_tokens"] for q in queries) / sample_size
    avg_output = sum(q["output_tokens"] for q in queries) / sample_size
    model = queries[0].get("model", "claude-sonnet-4-6")

    print(f"\nSample: {sample_size} queries | Model: {model}")
    print(f"Avg input: {avg_input:.0f} tokens | Avg output: {avg_output:.0f} tokens")
    print(f"Monthly volume: {monthly_volume:,} requests | Cache hit rate: {cache_hit_rate:.0%}")
    print("=" * 60)

    prices = PRICING.get(model, PRICING["claude-sonnet-4-6"])

    # Monthly totals
    total_input_tokens = avg_input * monthly_volume
    total_output_tokens = avg_output * monthly_volume

    # Cached vs uncached input
    cached_tokens = total_input_tokens * cache_hit_rate
    uncached_tokens = total_input_tokens * (1 - cache_hit_rate)

    cache_rate = prices["cache"] or prices["input"]  # fallback if no cache support
    input_cost = (uncached_tokens * prices["input"] + cached_tokens * cache_rate) / 1_000_000
    output_cost = total_output_tokens * prices["output"] / 1_000_000

    print(f"\nMonthly cost breakdown:")
    print(f"  Input (uncached):  ${uncached_tokens/1e6:.1f}M tokens x ${prices['input']:.4f} = ${uncached_tokens * prices['input'] / 1e6:.2f}")
    print(f"  Input (cached):    ${cached_tokens/1e6:.1f}M tokens x ${cache_rate:.4f} = ${cached_tokens * cache_rate / 1e6:.2f}")
    print(f"  Output:            ${total_output_tokens/1e6:.1f}M tokens x ${prices['output']:.4f} = ${output_cost:.2f}")
    print(f"  TOTAL:             ${input_cost + output_cost:.2f}/month")
    print(f"  Annual:            ${(input_cost + output_cost) * 12:,.0f}/year")

    # Compare all models
    print("\nComparison across all models (same volume, same cache rate):")
    print(f"{'Model':<25} {'Monthly':>12} {'Annual':>12} {'Per 1k req':>12}")
    print("-" * 65)
    for m, p in sorted(PRICING.items(), key=lambda x: x[1]["input"] * avg_input + x[1]["output"] * avg_output):
        cr = p["cache"] or p["input"]
        ic = (uncached_tokens * p["input"] + cached_tokens * cr) / 1_000_000
        oc = total_output_tokens * p["output"] / 1_000_000
        total = ic + oc
        per_1k = total / monthly_volume * 1000
        print(f"{m:<25} ${total:>10.2f} ${total*12:>10,.0f} ${per_1k:>10.4f}")

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--queries", default="queries.jsonl")
    parser.add_argument("--volume", type=int, default=100_000)
    parser.add_argument("--cache-hit", type=float, default=0.0)
    args = parser.parse_args()
    calculate_cost(args.queries, args.volume, args.cache_hit)

# Output example:
# Sample: 500 queries | Model: claude-sonnet-4-6
# Avg input: 2180 tokens | Avg output: 250 tokens
# Monthly volume: 400,000 requests | Cache hit rate: 80%
# ============================================================
# Monthly cost breakdown:
#   Input (uncached):    174.4M tokens × $3.0000 = $523.20
#   Input (cached):      697.6M tokens × $0.3000 = $209.28
#   Output:               100.0M tokens × $15.0000 = $1,500.00
#   TOTAL:             $2,232.48/month
#   Annual:            $26,790/year

Frequently Asked Questions