Accounts Fireworks Models GPT Oss 120B vs Baidu Ernie 4.5 VL 28B A3b Thinking

Accounts Fireworks Models GPT Oss 120B (Fireworks AI, 131,072-token context) versus Baidu Ernie 4.5 VL 28B A3b Thinking (Novita AI, 131,072-token context). Accounts Fireworks Models GPT Oss 120B is cheaper by 4% 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 — Accounts Fireworks Models GPT Oss 120B vs Baidu Ernie 4.5 VL 28B A3b Thinking

Accounts Fireworks Models GPT Oss 120B and Baidu Ernie 4.5 VL 28B A3b Thinking are priced within 4% 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. 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.

Side-by-side cost

Live workload comparison

Same workload run through both models. The cheaper one is highlighted.

Input tokens / request3,000

0131,072

Output tokens / request400

065,536

Requests / day5,000

01,000,000

Accounts Fireworks Models GPT Oss 120BCheaper

Fireworks AI

$105/mo

Input $0.150/M · Output $0.600/M

Baidu Ernie 4.5 VL 28B A3b Thinking

Novita AI

$202/mo

Input $0.390/M · Output $0.390/M

At this workload, Accounts Fireworks Models GPT Oss 120B is 48% cheaper than Baidu Ernie 4.5 VL 28B A3b Thinking — a savings of $96.79/month ($1,161/year).

Crossover: Accounts Fireworks Models GPT Oss 120B is cheaper when output/input ≤ 1.14 (input-heavy workloads — RAG, retrieval). Baidu Ernie 4.5 VL 28B A3b Thinking wins above (long-form generation).

Current workload ratio: 0.13 (400/3000)

Production recipe — Agent Command Center

strategy: cost-optimized
primary:
  model: accounts-fireworks-models-gpt-oss-120b
  provider: fireworks-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

Get started free →Routing docs ↗

	Accounts Fireworks Models GPT Oss 120B Fireworks AI	Baidu Ernie 4.5 VL 28B A3b Thinking Novita AI
Input price	$0.150/M	$0.390/M
Output price	$0.600/M	$0.390/M
Context window	131,072	131,072
Max output	32,768	65,536
Function calling	✓	✓
Vision	—	✓
Audio input	—	—
Reasoning	✓	✓
Prompt caching	—	—
Structured output	✓	✓
Pricing verified	Aug 6, 2026	Aug 6, 2026

Cheaper option

Accounts Fireworks Models GPT Oss 120B

~4% cheaper than the priciest in this pair

Larger context

Accounts Fireworks Models GPT Oss 120B

131,072 tokens

More capabilities

Baidu Ernie 4.5 VL 28B A3b Thinking

4 of 6 capability flags advertised

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	Accounts Fireworks Models GPT Oss 120B	Baidu Ernie 4.5 VL 28B A3b Thinking	Delta
Startup 10K requests/day	$81.00 /mo	$140 /mo	$59.40/mo
Mid-market 100K requests/day	$810 /mo	$1,404 /mo	$594/mo
Enterprise 1M requests/day	$8,100 /mo	$14,040 /mo	$5,940/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.

Choose Baidu Ernie 4.5 VL 28B A3b Thinking

Your inputs include screenshots, diagrams, or product photos — Baidu Ernie 4.5 VL 28B A3b Thinking accepts image input natively, 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 Accounts Fireworks Models GPT Oss 120B, switching to Baidu Ernie 4.5 VL 28B A3b Thinking means re-architecting that path (and vice versa).

Only on Accounts Fireworks Models GPT Oss 120B

Nothing — everything Accounts Fireworks Models GPT Oss 120B ships is also on Baidu Ernie 4.5 VL 28B A3b Thinking.

Only on Baidu Ernie 4.5 VL 28B A3b Thinking

• Parallel tool calls
• Vision input

Capabilities both share (4)

✓ Function calling
✓ Streaming
✓ Structured output (JSON schema)
✓ Native reasoning mode

Migration considerations

Concrete differences to wire through your stack before you flip traffic from one to the other.

Max output tokens differ: 32,768 on Accounts Fireworks Models GPT Oss 120B vs 65,536 on Baidu Ernie 4.5 VL 28B A3b Thinking. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
Baidu Ernie 4.5 VL 28B A3b Thinking has capabilities Accounts Fireworks Models GPT Oss 120B lacks: Parallel tool calls, Vision input. Worth wiring through the agent design before commit.
Provider changes from Fireworks AI to Novita 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 Accounts Fireworks Models GPT Oss 120B vs Baidu Ernie 4.5 VL 28B A3b Thinking 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 beyond base_url and a virtual key.
2. Mark Accounts Fireworks Models GPT Oss 120B primary, mirror 20% of traffic to Baidu Ernie 4.5 VL 28B A3b Thinking 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 — Accounts Fireworks Models GPT Oss 120B vs Baidu Ernie 4.5 VL 28B A3b Thinking

Which is cheaper, Accounts Fireworks Models GPT Oss 120B or Baidu Ernie 4.5 VL 28B A3b Thinking? ▾

Accounts Fireworks Models GPT Oss 120B is cheaper by roughly 4% on a blended input + output token mix. Input prices are $0.150/M for Accounts Fireworks Models GPT Oss 120B versus $0.390/M for Baidu Ernie 4.5 VL 28B A3b Thinking; output prices are $0.600/M versus $0.390/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 Accounts Fireworks Models GPT Oss 120B versus Baidu Ernie 4.5 VL 28B A3b Thinking? ▾

Accounts Fireworks Models GPT Oss 120B supports up to 131,072 tokens of context. Baidu Ernie 4.5 VL 28B A3b Thinking supports up to 131,072 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 Accounts Fireworks Models GPT Oss 120B and Baidu Ernie 4.5 VL 28B A3b Thinking both support tool calling? ▾

Yes — both Accounts Fireworks Models GPT Oss 120B and Baidu Ernie 4.5 VL 28B A3b Thinking 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 Accounts Fireworks Models GPT Oss 120B and Baidu Ernie 4.5 VL 28B A3b Thinking process images? ▾

Baidu Ernie 4.5 VL 28B A3b Thinking accepts native image input. Accounts Fireworks Models GPT Oss 120B 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 Accounts Fireworks Models GPT Oss 120B.

How do I A/B test Accounts Fireworks Models GPT Oss 120B against Baidu Ernie 4.5 VL 28B A3b Thinking 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.