HEAD-TO-HEAD · EFFICIENT-TIER · VERIFIED NOVEMBER 2, 2026

GPT-6.1 Terra vs Claude Haiku 4.5 — which is actually cheaper?

GPT-6.1 Terra and Claude Haiku 4.5 list identical per-token rates: $1 per million input tokens and $5 per million output tokens. On paper that makes this comparison a dead heat — the real differentiators are context window size and how each provider handles long-context or cached pricing tiers.

Spec	GPT-6.1 Terra	Claude Haiku 4.5
Input / 1M tokens	$1.00	$1.00
Output / 1M tokens	$5.00	$5.00
Context window	1.05M	200K

The output price gap

There is no output price gap here — both models charge exactly $5 per million output tokens, a 5x premium over their input rate. That 5:1 ratio is typical for this model class, and it means output-heavy workloads like long-form writing, code generation, or chain-of-thought reasoning will dominate your bill regardless of which model you pick.

Context window

The meaningful difference is context, not price. GPT-6.1 Terra ships with roughly a 1.05M-token context window, giving it substantially more headroom for long documents or multi-turn agent sessions. Claude Haiku 4.5's standard context window is closer to 200K tokens. If your workload involves very long prompts, Terra's larger window may let you avoid chunking strategies that Haiku 4.5 would require — though both figures should be confirmed against current provider documentation since limits and long-context surcharge tiers can shift.

Worked example

At 100M input tokens and 30M output tokens in a month, the math is identical for both models: input cost = 100M × $1/M = $100; output cost = 30M × $5/M = $150; total = $100 + $150 = $250/month. So GPT-6.1 Terra and Claude Haiku 4.5 both land at exactly $250/month for this workload — there is no price-based winner at standard rates, only differences in context window, caching discounts, and task performance.

Prices from the LLM Price Watch daily tracker, 2026-11-02. Prices change; use the calculator with your own usage for an exact comparison, or see the full price table for every tracked model.

Frequently asked questions

Is GPT-6.1 Terra cheaper than Claude Haiku 4.5?

No — at standard rates they are priced identically, both charging $1 per million input tokens and $5 per million output tokens. For a typical workload, the monthly bill comes out the same for either model, so the choice should come down to context window, latency, or task-specific performance rather than price.

Which model has a bigger context window?

GPT-6.1 Terra has the edge with roughly a 1.05M-token context window, compared to Claude Haiku 4.5's approximately 200K-token window. If you work with very long documents or extended multi-turn sessions, Terra's larger window may reduce the need for context truncation or chunking.

Does caching change the price comparison?

Potentially. GPT-6.1 Terra offers cached input at a steep discount, around $0.05 per million tokens, which can meaningfully lower costs for repeated-context workloads like chat history or document re-use. Check each provider's current cache pricing, since cached-input discounts aren't identical across models and can shift the effective cost.

What is the monthly cost at 100M input and 30M output tokens?

For both models: 100M input tokens at $1/M equals $100, and 30M output tokens at $5/M equals $150, for a combined total of $250 per month. Since pricing is identical, this worked example results in the exact same bill for GPT-6.1 Terra and Claude Haiku 4.5.