Baidu Ernie 4.5 VL 28B A3b Thinking vs Codestral latest
Baidu Ernie 4.5 VL 28B A3b Thinking (Novita AI, 131,072-token context) versus Codestral latest (Google Vertex AI, 128,000-token context). Baidu Ernie 4.5 VL 28B A3b Thinking is cheaper by 2% on a blended token mix. Baidu Ernie 4.5 VL 28B A3b Thinking 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.
Bottom line — Baidu Ernie 4.5 VL 28B A3b Thinking vs Codestral latest
Baidu Ernie 4.5 VL 28B A3b Thinking and Codestral latest are priced within 2% 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 parallel tool calls where the other does not; Baidu Ernie 4.5 VL 28B A3b Thinking supports vision input where the other does not; Baidu Ernie 4.5 VL 28B A3b Thinking 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: codestral-latest
provider: vertex-ai
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 | Codestral latest | |
|---|---|---|
| Input price | $0.390/M | $0.200/M |
| Output price | $0.390/M | $0.600/M |
| Context window | 131,072 | 128,000 |
| Max output | 65,536 | 128,000 |
| 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 | Codestral latest | Delta |
|---|---|---|---|
| Startup 10K requests/day | $140 /mo | $96.00 /mo | $44.40/mo |
| Mid-market 100K requests/day | $1,404 /mo | $960 /mo | $444/mo |
| Enterprise 1M requests/day | $14,040 /mo | $9,600 /mo | $4,440/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 inputs include screenshots, diagrams, or product photos — Baidu Ernie 4.5 VL 28B A3b Thinking 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 Thinking 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 Thinking, switching to Codestral latest means re-architecting that path (and vice versa).
- • Parallel tool calls
- • Vision input
- • Structured output (JSON schema)
- • Native reasoning mode
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.
- Max output tokens differ: 65,536 on Baidu Ernie 4.5 VL 28B A3b Thinking vs 128,000 on Codestral latest. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- Baidu Ernie 4.5 VL 28B A3b Thinking has capabilities Codestral latest lacks: Parallel tool calls, Vision input, Structured output (JSON schema), Native reasoning mode. Switching to Codestral latest means re-architecting any flow that depends on these.
- Provider changes from Novita AI to Google Vertex AI. 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 Codestral latest 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 Codestral latest 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 Codestral latest
Which is cheaper, Baidu Ernie 4.5 VL 28B A3b Thinking or Codestral latest? ▾
Baidu Ernie 4.5 VL 28B A3b Thinking is cheaper by roughly 2% on a blended input + output token mix. Input prices are $0.390/M for Baidu Ernie 4.5 VL 28B A3b Thinking versus $0.200/M for Codestral latest; output prices are $0.390/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 Thinking versus Codestral latest? ▾
Baidu Ernie 4.5 VL 28B A3b Thinking supports up to 131,072 tokens of context. Codestral latest 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 Codestral latest both support tool calling? ▾
Yes — both Baidu Ernie 4.5 VL 28B A3b Thinking and Codestral latest 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 Thinking and Codestral latest process images? ▾
Baidu Ernie 4.5 VL 28B A3b Thinking accepts native image input. Codestral latest does not — you would need to route image-heavy workloads through Baidu Ernie 4.5 VL 28B A3b Thinking or add a separate vision model in front of Codestral latest.
When should I choose Baidu Ernie 4.5 VL 28B A3b Thinking over Codestral latest? ▾
Your inputs include screenshots, diagrams, or product photos — Baidu Ernie 4.5 VL 28B A3b Thinking 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 Thinking ships a native reasoning mode that explicitly thinks before responding, the other doesn't.
When should I choose Codestral latest over Baidu Ernie 4.5 VL 28B A3b Thinking? ▾
On the data this page surfaces, Codestral latest is the right pick when Baidu Ernie 4.5 VL 28B A3b Thinking'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 Baidu Ernie 4.5 VL 28B A3b Thinking against Codestral latest 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.