Baidu Ernie 4.5 VL 28B A3b vs GPT 4o mini Audio preview (2024-12-17)
Baidu Ernie 4.5 VL 28B A3b (Novita AI, 30,000-token context) versus GPT 4o mini Audio preview (2024-12-17) (OpenAI, 128,000-token context). Baidu Ernie 4.5 VL 28B A3b is cheaper by 7% on a blended token mix. Baidu Ernie 4.5 VL 28B A3b uniquely supports vision input and native reasoning mode. GPT 4o mini Audio preview (2024-12-17) uniquely supports audio input and audio output. 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 vs GPT 4o mini Audio preview (2024-12-17)
Baidu Ernie 4.5 VL 28B A3b and GPT 4o mini Audio preview (2024-12-17) are priced within 7% 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 4o mini Audio preview (2024-12-17) ships a 128,000-token context window, 4.3x larger than Baidu Ernie 4.5 VL 28B A3b's 30,000 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 30,000 tokens, the extra context on GPT 4o mini Audio preview (2024-12-17) is insurance you may never use — and Baidu Ernie 4.5 VL 28B A3b may win on other axes.
On capability surface area, the models diverge: Baidu Ernie 4.5 VL 28B A3b supports vision input where the other does not; Baidu Ernie 4.5 VL 28B A3b supports native reasoning mode where the other does not; GPT 4o mini Audio preview (2024-12-17) supports audio input 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: baidu-ernie-4-5-vl-28b-a3b
provider: novita-ai
fallback:
model: gpt-4o-mini-audio-preview-2024-12-17
provider: openai
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| Baidu Ernie 4.5 VL 28B A3b | GPT 4o mini Audio preview (2024-12-17) | |
|---|---|---|
| Input price | $0.140/M | $0.150/M |
| Output price | $0.560/M | $0.600/M |
| Context window | 30,000 | 128,000 |
| Max output | 8,000 | 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 | Baidu Ernie 4.5 VL 28B A3b | GPT 4o mini Audio preview (2024-12-17) | Delta |
|---|---|---|---|
| Startup 10K requests/day | $75.60 /mo | $81.00 /mo | $5.40/mo |
| Mid-market 100K requests/day | $756 /mo | $810 /mo | $54.00/mo |
| Enterprise 1M requests/day | $7,560 /mo | $8,100 /mo | $540/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 4o mini Audio preview (2024-12-17) fits 128,000 tokens versus the other model's 30,000, enough headroom for full books, large codebases, or 100+ page documents in one shot.
Your inputs include screenshots, diagrams, or product photos — Baidu Ernie 4.5 VL 28B A3b accepts image input natively, the other doesn't.
Your agent listens to calls or voice notes — GPT 4o mini Audio preview (2024-12-17) accepts audio input directly, the other requires an ASR preprocessing hop.
Your tasks involve multi-step planning or math-heavy reasoning — Baidu Ernie 4.5 VL 28B A3b ships a native reasoning mode that explicitly thinks before responding, the other doesn't.
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, switching to GPT 4o mini Audio preview (2024-12-17) means re-architecting that path (and vice versa).
- • Vision input
- • Native reasoning mode
- • Audio input
- • Audio output
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 up 327% when moving from Baidu Ernie 4.5 VL 28B A3b (30,000) to GPT 4o mini Audio preview (2024-12-17) (128,000). Re-check any prompt that relies on cramming long history or documents.
- Max output tokens differ: 8,000 on Baidu Ernie 4.5 VL 28B A3b vs 16,384 on GPT 4o mini Audio preview (2024-12-17). Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- Baidu Ernie 4.5 VL 28B A3b has capabilities GPT 4o mini Audio preview (2024-12-17) lacks: Vision input, Native reasoning mode. Switching to GPT 4o mini Audio preview (2024-12-17) means re-architecting any flow that depends on these.
- GPT 4o mini Audio preview (2024-12-17) has capabilities Baidu Ernie 4.5 VL 28B A3b lacks: Audio input, Audio output. Worth wiring through the agent design before commit.
- Provider changes from Novita AI to 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 vs GPT 4o mini Audio preview (2024-12-17) 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 primary, mirror 20% of traffic to GPT 4o mini Audio preview (2024-12-17) 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 vs GPT 4o mini Audio preview (2024-12-17)
Which is cheaper, Baidu Ernie 4.5 VL 28B A3b or GPT 4o mini Audio preview (2024-12-17)? ▾
Baidu Ernie 4.5 VL 28B A3b is cheaper by roughly 7% on a blended input + output token mix. Input prices are $0.140/M for Baidu Ernie 4.5 VL 28B A3b versus $0.150/M for GPT 4o mini Audio preview (2024-12-17); output prices are $0.560/M versus $0.600/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 versus GPT 4o mini Audio preview (2024-12-17)? ▾
Baidu Ernie 4.5 VL 28B A3b supports up to 30,000 tokens of context. GPT 4o mini Audio preview (2024-12-17) supports up to 128,000 tokens. GPT 4o mini Audio preview (2024-12-17) has the larger window by a factor of 4.3x, 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 and GPT 4o mini Audio preview (2024-12-17) both support tool calling? ▾
Yes — both Baidu Ernie 4.5 VL 28B A3b and GPT 4o mini Audio preview (2024-12-17) 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 Baidu Ernie 4.5 VL 28B A3b and GPT 4o mini Audio preview (2024-12-17) process images? ▾
Baidu Ernie 4.5 VL 28B A3b accepts native image input. GPT 4o mini Audio preview (2024-12-17) does not — you would need to route image-heavy workloads through Baidu Ernie 4.5 VL 28B A3b or add a separate vision model in front of GPT 4o mini Audio preview (2024-12-17).
When should I choose Baidu Ernie 4.5 VL 28B A3b over GPT 4o mini Audio preview (2024-12-17)? ▾
Your inputs include screenshots, diagrams, or product photos — Baidu Ernie 4.5 VL 28B A3b accepts image input natively, the other doesn't. Your tasks involve multi-step planning or math-heavy reasoning — Baidu Ernie 4.5 VL 28B A3b ships a native reasoning mode that explicitly thinks before responding, the other doesn't.
When should I choose GPT 4o mini Audio preview (2024-12-17) over Baidu Ernie 4.5 VL 28B A3b? ▾
Your workload needs long context — GPT 4o mini Audio preview (2024-12-17) fits 128,000 tokens versus the other model's 30,000, enough headroom for full books, large codebases, or 100+ page documents in one shot. Your agent listens to calls or voice notes — GPT 4o mini Audio preview (2024-12-17) accepts audio input directly, the other requires an ASR preprocessing hop.
How do I A/B test Baidu Ernie 4.5 VL 28B A3b against GPT 4o mini Audio preview (2024-12-17) 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.