Claude 3.5 Haiku (2024-10-22) vs Qwen Qwen3.5 397B A17b
Claude 3.5 Haiku (2024-10-22) (Anthropic, 200,000-token context) versus Qwen Qwen3.5 397B A17b (OpenRouter, 262,144-token context). Qwen Qwen3.5 397B A17b is cheaper by 13% on a blended token mix. Claude 3.5 Haiku (2024-10-22) uniquely supports pdf input and structured output (json schema). Qwen Qwen3.5 397B A17b uniquely supports native reasoning mode. 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 — Claude 3.5 Haiku (2024-10-22) vs Qwen Qwen3.5 397B A17b
Claude 3.5 Haiku (2024-10-22) and Qwen Qwen3.5 397B A17b target overlapping workloads but differ sharply on economics. Qwen Qwen3.5 397B A17b runs roughly 13% cheaper on a blended input-plus-output token mix, which translates to approximately $840 per month at mid-market volume (100K requests/day). The gap compounds at enterprise scale, making the cost axis the first filter most teams apply when deciding between these two models.
On capability surface area, the models diverge: Claude 3.5 Haiku (2024-10-22) supports pdf input where the other does not; Claude 3.5 Haiku (2024-10-22) supports structured output (json schema) where the other does not; Claude 3.5 Haiku (2024-10-22) supports prompt caching 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-qwen3-5-397b-a17b
provider: openrouter
fallback:
model: claude-3-5-haiku-20241022
provider: anthropic
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| Claude 3.5 Haiku (2024-10-22) | Qwen Qwen3.5 397B A17b | |
|---|---|---|
| Input price | $0.800/M | $0.600/M |
| Output price | $4.00/M | $3.60/M |
| Context window | 200,000 | 262,144 |
| Max output | 8,192 | 65,536 |
| Function calling | ✓ | ✓ |
| Vision | ✓ | ✓ |
| Audio input | — | — |
| Reasoning | — | ✓ |
| Prompt caching | ✓ | — |
| Structured output | ✓ | — |
| Pricing verified | May 7, 2026 | Aug 6, 2026 |
Benchmark comparison
Side-by-side public benchmark scores. Greener bar = winner.
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 | Claude 3.5 Haiku (2024-10-22) | Qwen Qwen3.5 397B A17b | Delta |
|---|---|---|---|
| Startup 10K requests/day | $480 /mo | $396 /mo | $84.00/mo |
| Mid-market 100K requests/day | $4,800 /mo | $3,960 /mo | $840/mo |
| Enterprise 1M requests/day | $48,000 /mo | $39,600 /mo | $8,400/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 tasks involve multi-step planning or math-heavy reasoning — Qwen Qwen3.5 397B A17b ships a native reasoning mode that explicitly thinks before responding, the other doesn't.
You re-send the same large system prompt across requests — Claude 3.5 Haiku (2024-10-22) supports prompt caching, cutting input cost on repeat hits.
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 Claude 3.5 Haiku (2024-10-22), switching to Qwen Qwen3.5 397B A17b means re-architecting that path (and vice versa).
- • PDF input
- • Structured output (JSON schema)
- • Prompt caching
- • Native reasoning mode
Capabilities both share (3)
- ✓ Function calling
- ✓ Vision input
- ✓ Streaming
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Max output tokens differ: 8,192 on Claude 3.5 Haiku (2024-10-22) vs 65,536 on Qwen Qwen3.5 397B A17b. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- Claude 3.5 Haiku (2024-10-22) has capabilities Qwen Qwen3.5 397B A17b lacks: PDF input, Structured output (JSON schema), Prompt caching. Switching to Qwen Qwen3.5 397B A17b means re-architecting any flow that depends on these.
- Qwen Qwen3.5 397B A17b has capabilities Claude 3.5 Haiku (2024-10-22) lacks: Native reasoning mode. Worth wiring through the agent design before commit.
- Provider changes from Anthropic 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.
- Pricing on Claude 3.5 Haiku (2024-10-22) was last verified 112 days ago — confirm against the provider's published rate card before committing to a multi-month migration.
How to A/B test Claude 3.5 Haiku (2024-10-22) vs Qwen Qwen3.5 397B A17b 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 Claude 3.5 Haiku (2024-10-22) primary, mirror 20% of traffic to Qwen Qwen3.5 397B A17b 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 — Claude 3.5 Haiku (2024-10-22) vs Qwen Qwen3.5 397B A17b
Which is cheaper, Claude 3.5 Haiku (2024-10-22) or Qwen Qwen3.5 397B A17b? ▾
Qwen Qwen3.5 397B A17b is cheaper by roughly 13% on a blended input + output token mix. Input prices are $0.800/M for Claude 3.5 Haiku (2024-10-22) versus $0.600/M for Qwen Qwen3.5 397B A17b; output prices are $4.00/M versus $3.60/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 Claude 3.5 Haiku (2024-10-22) versus Qwen Qwen3.5 397B A17b? ▾
Claude 3.5 Haiku (2024-10-22) supports up to 200,000 tokens of context. Qwen Qwen3.5 397B A17b supports up to 262,144 tokens. Qwen Qwen3.5 397B A17b has the larger window by a factor of 1.3x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
Do Claude 3.5 Haiku (2024-10-22) and Qwen Qwen3.5 397B A17b both support tool calling? ▾
Yes — both Claude 3.5 Haiku (2024-10-22) and Qwen Qwen3.5 397B A17b 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.
Which model supports prompt caching for cost reduction? ▾
Claude 3.5 Haiku (2024-10-22) supports prompt caching; the other does not. If your agent has a stable system prompt + retrieval context block that repeats across requests, Claude 3.5 Haiku (2024-10-22) gives you a 50–90% discount on those repeated input tokens at the provider level.
When should I choose Claude 3.5 Haiku (2024-10-22) over Qwen Qwen3.5 397B A17b? ▾
You re-send the same large system prompt across requests — Claude 3.5 Haiku (2024-10-22) supports prompt caching, cutting input cost on repeat hits.
When should I choose Qwen Qwen3.5 397B A17b over Claude 3.5 Haiku (2024-10-22)? ▾
Your tasks involve multi-step planning or math-heavy reasoning — Qwen Qwen3.5 397B A17b ships a native reasoning mode that explicitly thinks before responding, the other doesn't.
How do I A/B test Claude 3.5 Haiku (2024-10-22) against Qwen Qwen3.5 397B A17b 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.