GPT-4 vs GPT 4.5 preview
GPT-4 (Azure OpenAI, 8,192-token context) versus GPT 4.5 preview (Azure OpenAI, 128,000-token context). GPT-4 is cheaper by 60% on a blended token mix. GPT 4.5 preview uniquely supports parallel tool calls and vision 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.
Live workload comparison
Same workload run through both models. The cheaper one is highlighted.
strategy: cost-optimized
primary:
model: gpt-4
provider: azure-openai
fallback:
model: gpt-4-5-preview
provider: azure-openai
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| GPT-4 | GPT 4.5 preview | |
|---|---|---|
| Input price | $30.00/M | $75.00/M |
| Output price | $60.00/M | $150/M |
| Context window | 8,192 | 128,000 |
| Max output | 4,096 | 16,384 |
| Function calling | ✓ | ✓ |
| Vision | — | ✓ |
| Audio input | — | — |
| Reasoning | — | — |
| Prompt caching | — | ✓ |
| Structured output | — | ✓ |
| Pricing verified | May 19, 2026 | May 19, 2026 |
Benchmark comparison
Side-by-side public benchmark scores. Greener bar = winner.
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-4 | GPT 4.5 preview | Delta |
|---|---|---|---|
| Startup 10K requests/day | $12,600 /mo | $31,500 /mo | $18,900/mo |
| Mid-market 100K requests/day | $126,000 /mo | $315,000 /mo | $189,000/mo |
| Enterprise 1M requests/day | $1,260,000 /mo | $3,150,000 /mo | $1,890,000/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.
You're cost-sensitive at scale — GPT-4 runs ~60% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.
Your workload needs long context — GPT 4.5 preview fits 128,000 tokens versus the other model's 8,192, enough headroom for full books, large codebases, or 100+ page documents in one shot.
Your inputs include screenshots, diagrams, or product photos — GPT 4.5 preview accepts image input natively, the other doesn't.
You re-send the same large system prompt across requests — GPT 4.5 preview 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-4, switching to GPT 4.5 preview means re-architecting that path (and vice versa).
- • Parallel tool calls
- • Vision input
- • Structured output (JSON schema)
- • Prompt caching
Capabilities both share (2)
- ✓ Function calling
- ✓ Streaming
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Context window changes up 1462% when moving from GPT-4 (8,192) to GPT 4.5 preview (128,000). Re-check any prompt that relies on cramming long history or documents.
- Max output tokens differ: 4,096 on GPT-4 vs 16,384 on GPT 4.5 preview. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- GPT 4.5 preview has capabilities GPT-4 lacks: Parallel tool calls, Vision input, Structured output (JSON schema), Prompt caching. Worth wiring through the agent design before commit.
How to A/B test GPT-4 vs GPT 4.5 preview 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-4 primary, mirror 20% of traffic to GPT 4.5 preview 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-4 vs GPT 4.5 preview
Which is cheaper, GPT-4 or GPT 4.5 preview? ▾
GPT-4 is cheaper by roughly 60% on a blended input + output token mix. Input prices are $30.00/M for GPT-4 versus $75.00/M for GPT 4.5 preview; output prices are $60.00/M versus $150/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-4 versus GPT 4.5 preview? ▾
GPT-4 supports up to 8,192 tokens of context. GPT 4.5 preview supports up to 128,000 tokens. GPT 4.5 preview has the larger window by a factor of 15.6x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
Do GPT-4 and GPT 4.5 preview both support tool calling? ▾
Yes — both GPT-4 and GPT 4.5 preview 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-4 and GPT 4.5 preview process images? ▾
GPT 4.5 preview accepts native image input. GPT-4 does not — you would need to route image-heavy workloads through GPT 4.5 preview or add a separate vision model in front of GPT-4.
Which model supports prompt caching for cost reduction? ▾
GPT 4.5 preview supports prompt caching; the other does not. If your agent has a stable system prompt + retrieval context block that repeats across requests, GPT 4.5 preview gives you a 50–90% discount on those repeated input tokens at the provider level.
When should I choose GPT-4 over GPT 4.5 preview? ▾
You're cost-sensitive at scale — GPT-4 runs ~60% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.
When should I choose GPT 4.5 preview over GPT-4? ▾
Your workload needs long context — GPT 4.5 preview fits 128,000 tokens versus the other model's 8,192, enough headroom for full books, large codebases, or 100+ page documents in one shot. Your inputs include screenshots, diagrams, or product photos — GPT 4.5 preview accepts image input natively, the other doesn't. You re-send the same large system prompt across requests — GPT 4.5 preview supports prompt caching, cutting input cost on repeat hits.
How do I A/B test GPT-4 against GPT 4.5 preview 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.