GPT-5.6 Terra vs Voxtral Small
Pricing verdict: GPT-5.6 Terra vs Voxtral Small: Voxtral Small is cheaper for input-heavy usage ($0.10/M vs $2.50/M input tokens), while GPT-5.6 Terra is better for long-context tasks (1,050,000 tokens).
Direct answer: choose Voxtral Small for lower token spend and choose GPT-5.6 Terra when your workload needs longer context.
Compare API pricing, input and output token costs, context windows, and monthly estimates on one page so you can pick the right model fast.
Cost Comparison (1000 input + 500 output tokens, 100 requests/day)
GPT-5.6 Terra
Voxtral Small
Cost Differences
Voxtral Small costs less than GPT-5.6 Terra
Quick Recommendation
Winner for direct API pricing: Voxtral Small. At the default workload, Voxtral Small saves about $29.10/month ($354.05/year) versus GPT-5.6 Terra.
Feature Comparison
| Feature | GPT-5.6 Terra | Voxtral Small |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Input Price | $2.50/1M tokens | $0.10/1M tokens |
| Output Price | $15.00/1M tokens | $0.40/1M tokens |
| Context Window | 1,050,000 tokens | 32,000 tokens |
| Max Output | 128,000 tokens | 8,192 tokens |
| Category | balanced | efficient |
| Capabilities | textvisioncodereasoning | textaudiotranscription |
| Release Date | 6/26/2026 | 7/15/2026 |
GPT-5.6 Terra vs Voxtral Small: Which Should You Choose?
Choosing between GPT-5.6 Terra and Voxtral Small depends on your priorities: cost efficiency, context length, or raw capability. Voxtral Small is the more affordable option at $0.10/1M input tokens — 96% cheaper than GPT-5.6 Terra. Meanwhile, GPT-5.6 Terra offers a significantly larger context window at 1,050,000 tokens vs 32,000 for Voxtral Small.
These models come from different providers — OpenAI and Mistral AI — which means different API ecosystems, SDKs, rate limits, and terms of service. If you're already integrated with OpenAI, switching to Mistral AIinvolves migration effort beyond just pricing. Factor in your existing infrastructure when deciding.
These models target different tiers: GPT-5.6 Terra is a balanced model while Voxtral Small is efficient. This means they're optimized for different workloads. Voxtral Small targets more demanding workloads, while GPT-5.6 Terra provides a cost-effective option for everyday tasks.
Output costs matter too. GPT-5.6 Terra charges $15.00/1M output tokens vs $0.40 for Voxtral Small. For generation-heavy workloads (content creation, code generation, summarization), output pricing often dominates your bill. Voxtral Small has the edge here at $0.40/1M output tokens.
Multimodal capabilities: GPT-5.6 Terra supports vision (image inputs) while Voxtral Small is text-only. If your application needs image understanding, this narrows your choice.
Best Use Cases
Choose GPT-5.6 Terra when:
- • You need a larger context window (1,050,000 tokens)
- • You need more capabilities (vision, code, reasoning)
- • You need longer outputs (up to 128,000 tokens)
- • You're already using OpenAI's API ecosystem
Choose Voxtral Small when:
- • Budget is a primary concern
- • You're already using Mistral AI's API ecosystem
- • You're running high-volume, latency-sensitive workloads
Pros and Caveats at a Glance
GPT-5.6 Terra
- • Input pricing: $2.50/M tokens
- • Output pricing: $15.00/M tokens
- • Context window: 1,050,000 tokens
- • Max output: 128,000 tokens
Watch out for
- • Higher input cost than Voxtral Small
- • Higher output cost than Voxtral Small
Voxtral Small
- • Input pricing: $0.10/M tokens
- • Output pricing: $0.40/M tokens
- • Context window: 32,000 tokens
- • Max output: 8,192 tokens
Watch out for
- • Smaller context window than GPT-5.6 Terra
Try Different Scenarios
Use the calculator below to see how costs change with different usage patterns
GPT-5.6 Terra (OpenAI)
Voxtral Small (Mistral AI)
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Which is cheaper, GPT-5.6 Terra or Voxtral Small?▼
What is the context window difference between GPT-5.6 Terra and Voxtral Small?▼
Which model is better for AI Chatbot?▼
Which model has better overall pricing for heavy usage?▼
Where can I compare OpenAI and Mistral AI API pricing beyond this model matchup?▼
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