Baidu Ernie 4.5 VL 28B A3b Thinking vs GPT-4o mini (2024-07-18)
Baidu Ernie 4.5 VL 28B A3b Thinking (Novita AI, 131,072-token context) versus GPT-4o mini (2024-07-18) (Azure OpenAI, 128,000-token context). Baidu Ernie 4.5 VL 28B A3b Thinking is cheaper by 5% on a blended token mix. Baidu Ernie 4.5 VL 28B A3b Thinking uniquely supports native reasoning mode. GPT-4o mini (2024-07-18) uniquely supports prompt caching. 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 — Baidu Ernie 4.5 VL 28B A3b Thinking vs GPT-4o mini (2024-07-18)
Baidu Ernie 4.5 VL 28B A3b Thinking and GPT-4o mini (2024-07-18) are priced within 5% 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.
On capability surface area, the models diverge: Baidu Ernie 4.5 VL 28B A3b Thinking supports native reasoning mode where the other does not; GPT-4o mini (2024-07-18) supports prompt caching 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: us-gpt-4o-mini-2024-07-18
provider: azure-openai
fallback:
model: baidu-ernie-4-5-vl-28b-a3b-thinking
provider: novita-ai
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| Baidu Ernie 4.5 VL 28B A3b Thinking | GPT-4o mini (2024-07-18) | |
|---|---|---|
| Input price | $0.390/M | $0.165/M |
| Output price | $0.390/M | $0.660/M |
| Context window | 131,072 | 128,000 |
| Max output | 65,536 | 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 | Baidu Ernie 4.5 VL 28B A3b Thinking | GPT-4o mini (2024-07-18) | Delta |
|---|---|---|---|
| Startup 10K requests/day | $140 /mo | $89.10 /mo | $51.30/mo |
| Mid-market 100K requests/day | $1,404 /mo | $891 /mo | $513/mo |
| Enterprise 1M requests/day | $14,040 /mo | $8,910 /mo | $5,130/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 tasks involve multi-step planning or math-heavy reasoning — Baidu Ernie 4.5 VL 28B A3b Thinking 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-4o mini (2024-07-18) 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 Baidu Ernie 4.5 VL 28B A3b Thinking, switching to GPT-4o mini (2024-07-18) means re-architecting that path (and vice versa).
- • Native reasoning mode
- • Prompt caching
Capabilities both share (5)
- ✓ Function calling
- ✓ Parallel tool calls
- ✓ Vision input
- ✓ Streaming
- ✓ Structured output (JSON schema)
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Max output tokens differ: 65,536 on Baidu Ernie 4.5 VL 28B A3b Thinking vs 16,384 on GPT-4o mini (2024-07-18). Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- Baidu Ernie 4.5 VL 28B A3b Thinking has capabilities GPT-4o mini (2024-07-18) lacks: Native reasoning mode. Switching to GPT-4o mini (2024-07-18) means re-architecting any flow that depends on these.
- GPT-4o mini (2024-07-18) has capabilities Baidu Ernie 4.5 VL 28B A3b Thinking lacks: Prompt caching. Worth wiring through the agent design before commit.
- Provider changes from Novita AI to Azure OpenAI. API authentication, rate-limit policy, regional availability, and billing all shift. Most teams route through an OpenAI-compatible gateway (e.g., Future AGI Agent Command Center) so the swap is a single `base_url` change instead of an SDK rewrite.
How to A/B test Baidu Ernie 4.5 VL 28B A3b Thinking vs GPT-4o mini (2024-07-18) 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 Baidu Ernie 4.5 VL 28B A3b Thinking primary, mirror 20% of traffic to GPT-4o mini (2024-07-18) 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 — Baidu Ernie 4.5 VL 28B A3b Thinking vs GPT-4o mini (2024-07-18)
Which is cheaper, Baidu Ernie 4.5 VL 28B A3b Thinking or GPT-4o mini (2024-07-18)? ▾
Baidu Ernie 4.5 VL 28B A3b Thinking is cheaper by roughly 5% on a blended input + output token mix. Input prices are $0.390/M for Baidu Ernie 4.5 VL 28B A3b Thinking versus $0.165/M for GPT-4o mini (2024-07-18); output prices are $0.390/M versus $0.660/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 Baidu Ernie 4.5 VL 28B A3b Thinking versus GPT-4o mini (2024-07-18)? ▾
Baidu Ernie 4.5 VL 28B A3b Thinking supports up to 131,072 tokens of context. GPT-4o mini (2024-07-18) supports up to 128,000 tokens. Baidu Ernie 4.5 VL 28B A3b Thinking has the larger window by a factor of 1.0x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
Do Baidu Ernie 4.5 VL 28B A3b Thinking and GPT-4o mini (2024-07-18) both support tool calling? ▾
Yes — both Baidu Ernie 4.5 VL 28B A3b Thinking and GPT-4o mini (2024-07-18) 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.
Which model supports prompt caching for cost reduction? ▾
GPT-4o mini (2024-07-18) supports prompt caching; the other does not. If your agent has a stable system prompt + retrieval context block that repeats across requests, GPT-4o mini (2024-07-18) gives you a 50–90% discount on those repeated input tokens at the provider level.
When should I choose Baidu Ernie 4.5 VL 28B A3b Thinking over GPT-4o mini (2024-07-18)? ▾
Your tasks involve multi-step planning or math-heavy reasoning — Baidu Ernie 4.5 VL 28B A3b Thinking ships a native reasoning mode that explicitly thinks before responding, the other doesn't.
When should I choose GPT-4o mini (2024-07-18) over Baidu Ernie 4.5 VL 28B A3b Thinking? ▾
You re-send the same large system prompt across requests — GPT-4o mini (2024-07-18) supports prompt caching, cutting input cost on repeat hits.
How do I A/B test Baidu Ernie 4.5 VL 28B A3b Thinking against GPT-4o mini (2024-07-18) 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.