Moonshotai Kimi K2.5 vs Qwen Qwen3.5 397B A17b
Moonshotai Kimi K2.5 (Amazon Bedrock, 262,144-token context) versus Qwen Qwen3.5 397B A17b (OpenRouter, 262,144-token context). Moonshotai Kimi K2.5 is cheaper by 14% 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 — Moonshotai Kimi K2.5 vs Qwen Qwen3.5 397B A17b
Moonshotai Kimi K2.5 and Qwen Qwen3.5 397B A17b target overlapping workloads but differ sharply on economics. Moonshotai Kimi K2.5 runs roughly 14% cheaper on a blended input-plus-output token mix, which translates to approximately $342 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.
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: moonshotai-kimi-k2-5
provider: bedrock
fallback:
model: qwen-qwen3-5-397b-a17b
provider: openrouter
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| Moonshotai Kimi K2.5 | Qwen Qwen3.5 397B A17b | |
|---|---|---|
| Input price | $0.600/M | $0.600/M |
| Output price | $3.03/M | $3.60/M |
| Context window | 262,144 | 262,144 |
| Max output | 262,144 | 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 | Moonshotai Kimi K2.5 | Qwen Qwen3.5 397B A17b | Delta |
|---|---|---|---|
| Startup 10K requests/day | $362 /mo | $396 /mo | $34.20/mo |
| Mid-market 100K requests/day | $3,618 /mo | $3,960 /mo | $342/mo |
| Enterprise 1M requests/day | $36,180 /mo | $39,600 /mo | $3,420/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.
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Max output tokens differ: 262,144 on Moonshotai Kimi K2.5 vs 65,536 on Qwen Qwen3.5 397B A17b. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- Provider changes from Amazon Bedrock 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 Moonshotai Kimi K2.5 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 Moonshotai Kimi K2.5 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 — Moonshotai Kimi K2.5 vs Qwen Qwen3.5 397B A17b
Which is cheaper, Moonshotai Kimi K2.5 or Qwen Qwen3.5 397B A17b? ▾
Moonshotai Kimi K2.5 is cheaper by roughly 14% on a blended input + output token mix. Input prices are $0.600/M for Moonshotai Kimi K2.5 versus $0.600/M for Qwen Qwen3.5 397B A17b; output prices are $3.03/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 Moonshotai Kimi K2.5 versus Qwen Qwen3.5 397B A17b? ▾
Moonshotai Kimi K2.5 supports up to 262,144 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 1.0x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
Do Moonshotai Kimi K2.5 and Qwen Qwen3.5 397B A17b both support tool calling? ▾
Yes — both Moonshotai Kimi K2.5 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.
How do I A/B test Moonshotai Kimi K2.5 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.