GPT-4o Mini vs Claude Haiku vs Gemini Flash: Best Budget Model for Production

Why the budget tier matters

The cheapest models in 2026 are significantly more capable than frontier models of two years ago. GPT-4o Mini, Claude 3.5 Haiku, and Gemini 2.0 Flash handle the majority of production workloads at a fraction of frontier model cost. For teams with high request volumes, the choice of budget model is one of the highest-leverage cost decisions available.

Cost comparison in INR

At ₹87/USD with 5% forex buffer and 5% markup:

| Model | Input (₹/M) | Output (₹/M) | vs GPT-4o | |-------|------------|-------------|-----------| | Gemini 2.0 Flash | ₹10 | ₹38 | 24× cheaper input | | GPT-4o Mini | ₹14 | ₹58 | 17× cheaper input | | Claude 3.5 Haiku | ₹96 | ₹480 | 2.5× cheaper input | | GPT-4o (reference) | ₹240 | ₹960 | baseline |

Testing all three models side by side

from openai import OpenAI
 
client = OpenAI(base_url="https://api.aicredits.in/v1", api_key="sk-your-aicredits-key")
 
BUDGET_MODELS = {
    "GPT-4o Mini":    "openai/gpt-4o-mini",
    "Claude Haiku":   "anthropic/claude-3-5-haiku-20241022",
    "Gemini Flash":   "google/gemini-2.0-flash-001",
}
 
def benchmark(prompt: str, task: str):
    print(f"\n=== {task} ===")
    for name, model in BUDGET_MODELS.items():
        response = client.chat.completions.create(
            model=model,
            messages=[{"role": "user", "content": prompt}],
        )
        usage = response.usage
        # Approximate INR cost
        rates = {"gpt-4o-mini": (14, 58), "haiku": (96, 480), "flash": (10, 38)}
        key = next(k for k in rates if k in model.lower())
        in_r, out_r = rates[key]
        cost = (usage.prompt_tokens * in_r + usage.completion_tokens * out_r) / 1_000_000
        print(f"  {name}: {response.choices[0].message.content[:80]}...")
        print(f"    tokens={usage.prompt_tokens}+{usage.completion_tokens} | ₹{cost:.5f}")
 
benchmark(
    "Classify as billing, technical, or general: 'My payment failed three times today.'",
    "Classification"
)
 
benchmark(
    "Summarise in one sentence: 'An LLM API gateway is middleware between your application and multiple AI providers. It handles routing, authentication, billing, rate limiting, and failover. Instead of managing separate API keys for OpenAI, Anthropic, and Google, you use a single endpoint.'",
    "Summarisation"
)

Classification tasks: GPT-4o Mini wins

For sentiment classification, intent detection, and topic categorisation on standard business text, GPT-4o Mini edges out the other two on consistency. It rarely produces ambiguous outputs, handles edge cases cleanly, and has the lowest latency (typically 300–500ms).

At ₹14/M input tokens, classification pipelines cost near-zero even at high volume.

Summarisation tasks: Claude 3.5 Haiku wins

Claude 3.5 Haiku produces noticeably higher-quality summaries than GPT-4o Mini and Gemini Flash. It preserves nuance better, handles longer source documents more accurately, and produces more consistently structured output.

The 7× price premium over GPT-4o Mini is often justified for summarisation tasks where quality is user-facing.

Code generation: Claude 3.5 Haiku wins

Claude 3.5 Haiku is the strongest code model in this tier by a clear margin. It handles multi-file context better than GPT-4o Mini, produces more idiomatic Python, and is significantly better at debugging tasks.

GPT-4o Mini is adequate for simple, self-contained code tasks. Gemini Flash lags both on code tasks.

The verdict: use all three

The highest-ROI approach is routing to the best model per task type:

TASK_ROUTING = {
    "classify":  "openai/gpt-4o-mini",              # cheapest, most consistent
    "summarise": "anthropic/claude-3-5-haiku-20241022",  # best quality-cost for text
    "code":      "anthropic/claude-3-5-haiku-20241022",  # best in tier for code
    "bulk":      "google/gemini-2.0-flash-001",      # cheapest for high-volume simple tasks
}
 
def routed_ask(task: str, prompt: str) -> str:
    model = TASK_ROUTING.get(task, "openai/gpt-4o-mini")
    response = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": prompt}],
    )
    return response.choices[0].message.content

This tiered approach delivers Haiku-level quality on important tasks at GPT-4o Mini average cost. All models are available through AICredits on a single endpoint with one INR wallet.