Qwen-Plus vs Qwen Qwen3 Next 80B A3b Thinking

Qwen-Plus (Alibaba DashScope, 129,024-token context) versus Qwen Qwen3 Next 80B A3b Thinking (Novita AI, 131,072-token context). Qwen-Plus is cheaper by 3% on a blended token mix. Qwen Qwen3 Next 80B A3b Thinking uniquely supports parallel tool calls and structured output (json schema). 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 — Qwen-Plus vs Qwen Qwen3 Next 80B A3b Thinking

Qwen-Plus and Qwen Qwen3 Next 80B A3b Thinking are priced within 3% 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: Qwen Qwen3 Next 80B A3b Thinking supports parallel tool calls where the other does not; Qwen Qwen3 Next 80B 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.

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

032,768

Requests / day5,000

01,000,000

Qwen-Plus

Alibaba DashScope

$256/mo

Input $0.400/M · Output $1.20/M

Qwen Qwen3 Next 80B A3b ThinkingCheaper

Novita AI

$160/mo

Input $0.150/M · Output $1.50/M

At this workload, Qwen Qwen3 Next 80B A3b Thinking is 37% cheaper than Qwen-Plus — a savings of $95.88/month ($1,151/year).

Crossover: Qwen Qwen3 Next 80B A3b Thinking is cheaper when output/input ≤ 0.83 (input-heavy workloads — RAG, retrieval). Qwen-Plus wins above (long-form generation).

Current workload ratio: 0.13 (400/3000)

Production recipe — Agent Command Center

strategy: cost-optimized
primary:
  model: qwen-qwen3-next-80b-a3b-thinking
  provider: novita-ai
fallback:
  model: qwen-plus
  provider: dashscope
shadow: { sample_rate: 0.05 }   # mirror 5% of traffic to compare quality live

Get started free →Routing docs ↗

	Qwen-Plus Alibaba DashScope	Qwen Qwen3 Next 80B A3b Thinking Novita AI
Input price	$0.400/M	$0.150/M
Output price	$1.20/M	$1.50/M
Context window	129,024	131,072
Max output	16,384	32,768
Function calling	✓	✓
Vision	—	—
Audio input	—	—
Reasoning	✓	✓
Prompt caching	—	—
Structured output	—	✓
Pricing verified	Jun 2, 2026	Jun 2, 2026

Cheaper option

Qwen-Plus

~3% cheaper than the priciest in this pair

Larger context

Qwen Qwen3 Next 80B A3b Thinking

131,072 tokens

More capabilities

Qwen Qwen3 Next 80B A3b Thinking

3 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	Qwen-Plus	Qwen Qwen3 Next 80B A3b Thinking	Delta
Startup 10K requests/day	$192 /mo	$135 /mo	$57.00/mo
Mid-market 100K requests/day	$1,920 /mo	$1,350 /mo	$570/mo
Enterprise 1M requests/day	$19,200 /mo	$13,500 /mo	$5,700/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.

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 Qwen-Plus, switching to Qwen Qwen3 Next 80B A3b Thinking means re-architecting that path (and vice versa).

Only on Qwen-Plus

Nothing — everything Qwen-Plus ships is also on Qwen Qwen3 Next 80B A3b Thinking.

Only on Qwen Qwen3 Next 80B A3b Thinking

• Parallel tool calls
• Structured output (JSON schema)

Capabilities both share (3)

✓ Function calling
✓ Streaming
✓ 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: 16,384 on Qwen-Plus vs 32,768 on Qwen Qwen3 Next 80B A3b Thinking. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
Qwen Qwen3 Next 80B A3b Thinking has capabilities Qwen-Plus lacks: Parallel tool calls, Structured output (JSON schema). Worth wiring through the agent design before commit.
Provider changes from Alibaba DashScope 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 Qwen-Plus vs Qwen Qwen3 Next 80B 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 Qwen-Plus primary, mirror 20% of traffic to Qwen Qwen3 Next 80B 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 — Qwen-Plus vs Qwen Qwen3 Next 80B A3b Thinking

Which is cheaper, Qwen-Plus or Qwen Qwen3 Next 80B A3b Thinking? ▾

Qwen-Plus is cheaper by roughly 3% on a blended input + output token mix. Input prices are $0.400/M for Qwen-Plus versus $0.150/M for Qwen Qwen3 Next 80B A3b Thinking; output prices are $1.20/M versus $1.50/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 Qwen-Plus versus Qwen Qwen3 Next 80B A3b Thinking? ▾

Qwen-Plus supports up to 129,024 tokens of context. Qwen Qwen3 Next 80B A3b Thinking supports up to 131,072 tokens. Qwen Qwen3 Next 80B 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 Qwen-Plus and Qwen Qwen3 Next 80B A3b Thinking both support tool calling? ▾

Yes — both Qwen-Plus and Qwen Qwen3 Next 80B 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.

How do I A/B test Qwen-Plus against Qwen Qwen3 Next 80B 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.