Kimi K3 vs Qwen3.5 35B A3B
Kimi K3 (Novita AI, 1,048,576-token context) versus Qwen3.5 35B A3B (DeepInfra, 262,144-token context). Qwen3.5 35B A3B is cheaper by 94% on a blended token mix. 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 — Kimi K3 vs Qwen3.5 35B A3B
Kimi K3 and Qwen3.5 35B A3B target overlapping workloads but differ sharply on economics. Qwen3.5 35B A3B runs roughly 94% cheaper on a blended input-plus-output token mix, which translates to approximately $16,980 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.
Kimi K3 ships a 1,048,576-token context window, 4.0x larger than Qwen3.5 35B A3B's 262,144 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 262,144 tokens, the extra context on Kimi K3 is insurance you may never use — and Qwen3.5 35B A3B may win on other axes.
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: deepinfra
fallback:
model: moonshotai-kimi-k3
provider: novita-ai
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| Kimi K3 | Qwen3.5 35B A3B | |
|---|---|---|
| Input price | $3.00/M | $0.140/M |
| Output price | $15.00/M | $1.00/M |
| Context window | 1,048,576 | 262,144 |
| Max output | 1,048,576 | — |
| Function calling | ✓ | ✓ |
| Vision | ✓ | ✓ |
| Audio input | — | — |
| Reasoning | ✓ | ✓ |
| Prompt caching | ✓ | ✓ |
| Structured output | ✓ | ✓ |
| Pricing verified | Oct 22, 2026 | Oct 22, 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 | Kimi K3 | Qwen3.5 35B A3B | Delta |
|---|---|---|---|
| Startup 10K requests/day | $1,800 /mo | $102 /mo | $1,698/mo |
| Mid-market 100K requests/day | $18,000 /mo | $1,020 /mo | $16,980/mo |
| Enterprise 1M requests/day | $180,000 /mo | $10,200 /mo | $169,800/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.
You're cost-sensitive at scale — Qwen3.5 35B A3B runs ~94% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.
Your workload needs long context — Kimi K3 fits 1,048,576 tokens versus the other model's 262,144, enough headroom for full books, large codebases, or 100+ page documents in one shot.
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Context window changes down 75% when moving from Kimi K3 (1,048,576) to Qwen3.5 35B A3B (262,144). Re-check any prompt that relies on cramming long history or documents.
- Provider changes from Novita AI to DeepInfra. 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 Kimi K3 vs 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 Kimi K3 primary, mirror 20% of traffic to 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 — Kimi K3 vs Qwen3.5 35B A3B
Which is cheaper, Kimi K3 or Qwen3.5 35B A3B? ▾
Qwen3.5 35B A3B is cheaper by roughly 94% on a blended input + output token mix. Input prices are $3.00/M for Kimi K3 versus $0.140/M for Qwen3.5 35B A3B; output prices are $15.00/M versus $1.00/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 Kimi K3 versus Qwen3.5 35B A3B? ▾
Kimi K3 supports up to 1,048,576 tokens of context. Qwen3.5 35B A3B supports up to 262,144 tokens. Kimi K3 has the larger window by a factor of 4.0x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
Do Kimi K3 and Qwen3.5 35B A3B both support tool calling? ▾
Yes — both Kimi K3 and 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.
Which model supports prompt caching for cost reduction? ▾
Both Kimi K3 and Qwen3.5 35B A3B support prompt caching. Cached input tokens are typically discounted 50–90% versus uncached input, depending on the provider. For agents with a stable system prompt + retrieval context, the cached pricing tier is the real unit economics number to track.
When should I choose Kimi K3 over Qwen3.5 35B A3B? ▾
Your workload needs long context — Kimi K3 fits 1,048,576 tokens versus the other model's 262,144, enough headroom for full books, large codebases, or 100+ page documents in one shot.
When should I choose Qwen3.5 35B A3B over Kimi K3? ▾
You're cost-sensitive at scale — Qwen3.5 35B A3B runs ~94% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.
How do I A/B test Kimi K3 against 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.