GPT-5 (2025-08-07) vs GPT Audio 1.5 (2026-02-23)
GPT-5 (2025-08-07) (Azure OpenAI, 272,000-token context) versus GPT Audio 1.5 (2026-02-23) (Azure OpenAI, 128,000-token context). GPT-5 (2025-08-07) is cheaper by 1% on a blended token mix. GPT-5 (2025-08-07) uniquely supports vision input and pdf input. Use the live calculator below to plug your real usage shape into both, then route the winner via Agent Command Center for shadow A/B without code changes.
Bottom line — GPT-5 (2025-08-07) vs GPT Audio 1.5 (2026-02-23)
GPT-5 (2025-08-07) and GPT Audio 1.5 (2026-02-23) are priced within 1% of each other, so cost alone is not the deciding factor. The comparison comes down to capabilities, context window, and benchmark performance on the specific task shape your workload demands.
GPT-5 (2025-08-07) ships a 272,000-token context window, 2.1x larger than GPT Audio 1.5 (2026-02-23)'s 128,000 tokens. That headroom matters for long-document RAG pipelines, multi-turn agent sessions that accumulate tool-call history, and codebases where the entire repository needs to fit in a single prompt. If your average prompt stays under 128,000 tokens, the extra context on GPT-5 (2025-08-07) is insurance you may never use — and GPT Audio 1.5 (2026-02-23) may win on other axes.
On capability surface area, the models diverge: GPT-5 (2025-08-07) supports vision input where the other does not; GPT-5 (2025-08-07) supports pdf input where the other does not; GPT-5 (2025-08-07) supports structured output (json schema) where the other does not. These differences are binary — either your workload needs the capability or it does not. Check whether any critical path in your agent pipeline depends on a capability only one model provides before committing to a migration.
For teams evaluating both models, the recommended path is a shadow A/B test: route production traffic through an OpenAI-compatible gateway, mirror a percentage to the candidate model, score both responses with an automated evaluator (faithfulness, tool-call correctness, latency), and compare cohort-level metrics over two weeks. Future AGI Agent Command Center supports this pattern with a single `base_url` change and built-in evaluators from the ai-evaluation SDK.
Live workload comparison
Same workload run through both models. The cheaper one is highlighted.
strategy: cost-optimized
primary:
model: eu-gpt-5-2025-08-07
provider: azure-openai
fallback:
model: gpt-audio-1-5-2026-02-23
provider: azure-openai
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| GPT-5 (2025-08-07) | GPT Audio 1.5 (2026-02-23) | |
|---|---|---|
| Input price | $1.38/M | $2.50/M |
| Output price | $11.00/M | $10.00/M |
| Context window | 272,000 | 128,000 |
| Max output | 128,000 | 16,384 |
| Function calling | ✓ | ✓ |
| Vision | ✓ | — |
| Audio input | — | — |
| Reasoning | ✓ | — |
| Prompt caching | ✓ | — |
| Structured output | ✓ | — |
| Pricing verified | Aug 6, 2026 | Aug 6, 2026 |
Cost at scale: monthly spend at three usage volumes
Estimated monthly cost assuming 1,000 input + 200 output tokens per request — a realistic chat-agent shape. Adjust your own usage in the calculator at the top of this page for an exact number.
| Scale | GPT-5 (2025-08-07) | GPT Audio 1.5 (2026-02-23) | Delta |
|---|---|---|---|
| Startup 10K requests/day | $1,073 /mo | $1,350 /mo | $278/mo |
| Mid-market 100K requests/day | $10,725 /mo | $13,500 /mo | $2,775/mo |
| Enterprise 1M requests/day | $107,250 /mo | $135,000 /mo | $27,750/mo |
At enterprise scale (1M requests/day), a difference of even ~10% in unit price compounds into thousands of dollars per month. Cached input pricing and batch tiers can shift this further — both are surfaced on each model's own page.
When to choose which
Picked from the data above — not vendor marketing. Match the rules to your workload, not the other way around.
Your workload needs long context — GPT-5 (2025-08-07) fits 272,000 tokens versus the other model's 128,000, enough headroom for full books, large codebases, or 100+ page documents in one shot.
Your inputs include screenshots, diagrams, or product photos — GPT-5 (2025-08-07) accepts image input natively, the other doesn't.
Your tasks involve multi-step planning or math-heavy reasoning — GPT-5 (2025-08-07) ships a native reasoning mode that explicitly thinks before responding, the other doesn't.
You re-send the same large system prompt across requests — GPT-5 (2025-08-07) supports prompt caching, cutting input cost on repeat hits.
Capability diff — what you gain and lose on the swap
A specific list of what each model has that the other doesn't. If your workload depends on a row in Only GPT-5 (2025-08-07), switching to GPT Audio 1.5 (2026-02-23) means re-architecting that path (and vice versa).
- • Vision input
- • PDF input
- • Structured output (JSON schema)
- • Prompt caching
- • Native reasoning mode
Capabilities both share (3)
- ✓ Function calling
- ✓ Parallel tool calls
- ✓ Streaming
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Context window changes down 53% when moving from GPT-5 (2025-08-07) (272,000) to GPT Audio 1.5 (2026-02-23) (128,000). Re-check any prompt that relies on cramming long history or documents.
- Max output tokens differ: 128,000 on GPT-5 (2025-08-07) vs 16,384 on GPT Audio 1.5 (2026-02-23). Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- GPT-5 (2025-08-07) has capabilities GPT Audio 1.5 (2026-02-23) lacks: Vision input, PDF input, Structured output (JSON schema), Prompt caching, Native reasoning mode. Switching to GPT Audio 1.5 (2026-02-23) means re-architecting any flow that depends on these.
How to A/B test GPT-5 (2025-08-07) vs GPT Audio 1.5 (2026-02-23) in production
If you're stuck between the two, run them side-by-side on real traffic. Four steps the Future AGI team uses internally:
- 1. Point your existing OpenAI SDK at
https://gateway.futureagi.com/v1. No code change beyondbase_urland a virtual key. - 2. Mark GPT-5 (2025-08-07) primary, mirror 20% of traffic to GPT Audio 1.5 (2026-02-23) in shadow mode. Both responses are logged; only the primary is served to users.
- 3. Score every shadow response with an evaluator — faithfulness, tool-call correctness, response latency, cost. Built-in evaluators in ai-evaluation cover the common axes.
- 4. Compare cohort-level metrics after two weeks. Switch primary when the candidate wins on what matters to your workload — and stays within your latency budget.
Full walkthrough on the Agent Command Center page.
FAQ — GPT-5 (2025-08-07) vs GPT Audio 1.5 (2026-02-23)
Which is cheaper, GPT-5 (2025-08-07) or GPT Audio 1.5 (2026-02-23)? ▾
GPT-5 (2025-08-07) is cheaper by roughly 1% on a blended input + output token mix. Input prices are $1.38/M for GPT-5 (2025-08-07) versus $2.50/M for GPT Audio 1.5 (2026-02-23); output prices are $11.00/M versus $10.00/M. The exact savings depend on your input:output ratio — use the live calculator above to plug in your own request shape.
What is the context window of GPT-5 (2025-08-07) versus GPT Audio 1.5 (2026-02-23)? ▾
GPT-5 (2025-08-07) supports up to 272,000 tokens of context. GPT Audio 1.5 (2026-02-23) supports up to 128,000 tokens. GPT-5 (2025-08-07) has the larger window by a factor of 2.1x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
Do GPT-5 (2025-08-07) and GPT Audio 1.5 (2026-02-23) both support tool calling? ▾
Yes — both GPT-5 (2025-08-07) and GPT Audio 1.5 (2026-02-23) support native function calling. Both also support structured output via JSON schema, so an agent can be ported between them with the same tool definitions.
Can GPT-5 (2025-08-07) and GPT Audio 1.5 (2026-02-23) process images? ▾
GPT-5 (2025-08-07) accepts native image input. GPT Audio 1.5 (2026-02-23) does not — you would need to route image-heavy workloads through GPT-5 (2025-08-07) or add a separate vision model in front of GPT Audio 1.5 (2026-02-23).
Which model supports prompt caching for cost reduction? ▾
GPT-5 (2025-08-07) supports prompt caching; the other does not. If your agent has a stable system prompt + retrieval context block that repeats across requests, GPT-5 (2025-08-07) gives you a 50–90% discount on those repeated input tokens at the provider level.
When should I choose GPT-5 (2025-08-07) over GPT Audio 1.5 (2026-02-23)? ▾
Your workload needs long context — GPT-5 (2025-08-07) fits 272,000 tokens versus the other model's 128,000, enough headroom for full books, large codebases, or 100+ page documents in one shot. Your inputs include screenshots, diagrams, or product photos — GPT-5 (2025-08-07) accepts image input natively, the other doesn't. Your tasks involve multi-step planning or math-heavy reasoning — GPT-5 (2025-08-07) ships a native reasoning mode that explicitly thinks before responding, the other doesn't. You re-send the same large system prompt across requests — GPT-5 (2025-08-07) supports prompt caching, cutting input cost on repeat hits.
When should I choose GPT Audio 1.5 (2026-02-23) over GPT-5 (2025-08-07)? ▾
On the data this page surfaces, GPT Audio 1.5 (2026-02-23) is the right pick when GPT-5 (2025-08-07)'s lower price or different capability profile aren't a fit for your workload. Run the live calculator above against your actual usage shape to confirm.
How do I A/B test GPT-5 (2025-08-07) against GPT Audio 1.5 (2026-02-23) in production? ▾
Route both through an OpenAI-compatible gateway like Future AGI Agent Command Center with shadow mode enabled. Send 100% of traffic to your primary model, mirror 10–20% to the candidate, score every response with an evaluator (faithfulness, tool-call correctness, response time), and compare cohort-level metrics for two weeks. Switch when the candidate wins on the metrics that matter to your workload and stays within your latency budget.