Accounts Fireworks Models DeepSeek V3p2 vs Qwen Qwen3.5 35B A3b
Accounts Fireworks Models DeepSeek V3p2 (Fireworks AI, 163,840-token context) versus Qwen Qwen3.5 35B A3b (OpenRouter, 262,144-token context). Accounts Fireworks Models DeepSeek V3p2 is cheaper by 0% on a blended token mix. Accounts Fireworks Models DeepSeek V3p2 uniquely supports structured output (json schema). Qwen Qwen3.5 35B A3b uniquely supports 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 DeepSeek V3p2 vs Qwen Qwen3.5 35B A3b
Accounts Fireworks Models DeepSeek V3p2 and Qwen Qwen3.5 35B A3b are priced within 0% 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.
Qwen Qwen3.5 35B A3b ships a 262,144-token context window, 1.6x larger than Accounts Fireworks Models DeepSeek V3p2's 163,840 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 163,840 tokens, the extra context on Qwen Qwen3.5 35B A3b is insurance you may never use — and Accounts Fireworks Models DeepSeek V3p2 may win on other axes.
On capability surface area, the models diverge: Accounts Fireworks Models DeepSeek V3p2 supports structured output (json schema) where the other does not; Qwen Qwen3.5 35B A3b 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.
Live workload comparison
Same workload run through both models. The cheaper one is highlighted.
strategy: cost-optimized
primary:
model: qwen-qwen3-5-35b-a3b
provider: openrouter
fallback:
model: accounts-fireworks-models-deepseek-v3p2
provider: fireworks-ai
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| Accounts Fireworks Models DeepSeek V3p2 | Qwen Qwen3.5 35B A3b | |
|---|---|---|
| Input price | $0.560/M | $0.250/M |
| Output price | $1.68/M | $2.00/M |
| Context window | 163,840 | 262,144 |
| Max output | 163,840 | 65,536 |
| 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 | Accounts Fireworks Models DeepSeek V3p2 | Qwen Qwen3.5 35B A3b | Delta |
|---|---|---|---|
| Startup 10K requests/day | $269 /mo | $195 /mo | $73.80/mo |
| Mid-market 100K requests/day | $2,688 /mo | $1,950 /mo | $738/mo |
| Enterprise 1M requests/day | $26,880 /mo | $19,500 /mo | $7,380/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 — Qwen Qwen3.5 35B A3b 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 DeepSeek V3p2, switching to Qwen Qwen3.5 35B A3b means re-architecting that path (and vice versa).
- • Structured output (JSON schema)
- • Vision input
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.
- Context window changes up 60% when moving from Accounts Fireworks Models DeepSeek V3p2 (163,840) to Qwen Qwen3.5 35B A3b (262,144). Re-check any prompt that relies on cramming long history or documents.
- Max output tokens differ: 163,840 on Accounts Fireworks Models DeepSeek V3p2 vs 65,536 on Qwen Qwen3.5 35B A3b. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- Accounts Fireworks Models DeepSeek V3p2 has capabilities Qwen Qwen3.5 35B A3b lacks: Structured output (JSON schema). Switching to Qwen Qwen3.5 35B A3b means re-architecting any flow that depends on these.
- Qwen Qwen3.5 35B A3b has capabilities Accounts Fireworks Models DeepSeek V3p2 lacks: Vision input. Worth wiring through the agent design before commit.
- Provider changes from Fireworks AI to OpenRouter. 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 DeepSeek V3p2 vs Qwen Qwen3.5 35B A3b 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 Accounts Fireworks Models DeepSeek V3p2 primary, mirror 20% of traffic to Qwen Qwen3.5 35B A3b 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 DeepSeek V3p2 vs Qwen Qwen3.5 35B A3b
What is the context window of Accounts Fireworks Models DeepSeek V3p2 versus Qwen Qwen3.5 35B A3b? ▾
Accounts Fireworks Models DeepSeek V3p2 supports up to 163,840 tokens of context. Qwen Qwen3.5 35B A3b supports up to 262,144 tokens. Qwen Qwen3.5 35B A3b has the larger window by a factor of 1.6x, 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 DeepSeek V3p2 and Qwen Qwen3.5 35B A3b both support tool calling? ▾
Yes — both Accounts Fireworks Models DeepSeek V3p2 and Qwen Qwen3.5 35B A3b 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 DeepSeek V3p2 and Qwen Qwen3.5 35B A3b process images? ▾
Qwen Qwen3.5 35B A3b accepts native image input. Accounts Fireworks Models DeepSeek V3p2 does not — you would need to route image-heavy workloads through Qwen Qwen3.5 35B A3b or add a separate vision model in front of Accounts Fireworks Models DeepSeek V3p2.
How do I A/B test Accounts Fireworks Models DeepSeek V3p2 against Qwen Qwen3.5 35B A3b 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.