GPT-5.4 vs Kimi K3
GPT-5.4 (Azure AI Foundry, 922,000-token context) versus Kimi K3 (Fireworks AI, 1,048,576-token context). GPT-5.4 is cheaper by 3% on a blended token mix. GPT-5.4 uniquely supports parallel tool calls and pdf input. Across 4 public benchmarks we tracked, GPT-5.4 wins 0 and Kimi K3 wins 4. 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 — GPT-5.4 vs Kimi K3
GPT-5.4 and Kimi K3 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: GPT-5.4 supports parallel tool calls where the other does not; GPT-5.4 supports pdf input where the other does not; GPT-5.4 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.
Across 4 public benchmarks, GPT-5.4 leads on 0 and Kimi K3 leads on 4. The widest gap is on arena-webdev, where Kimi K3 scores 254.0 points higher. Benchmarks are noisy and task-dependent — a model that leads on arena-elo may trail on code generation. The safest approach is to run both models on your own golden set before treating any benchmark as decisive.
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: gpt-5-4
provider: azure-ai-foundry
fallback:
model: kimi-k3
provider: fireworks-ai
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| GPT-5.4 | Kimi K3 | |
|---|---|---|
| Input price | $2.50/M | $3.00/M |
| Output price | $15.00/M | $15.00/M |
| Context window | 922,000 | 1,048,576 |
| Max output | 128,000 | 131,072 |
| Function calling | ✓ | ✓ |
| Vision | ✓ | ✓ |
| Audio input | — | — |
| Reasoning | ✓ | ✓ |
| Prompt caching | ✓ | — |
| Structured output | ✓ | ✓ |
| Pricing verified | Oct 22, 2026 | Oct 22, 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 | GPT-5.4 | Kimi K3 | Delta |
|---|---|---|---|
| Startup 10K requests/day | $1,650 /mo | $1,800 /mo | $150/mo |
| Mid-market 100K requests/day | $16,500 /mo | $18,000 /mo | $1,500/mo |
| Enterprise 1M requests/day | $165,000 /mo | $180,000 /mo | $15,000/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-send the same large system prompt across requests — GPT-5.4 supports prompt caching, cutting input cost on repeat hits.
On arena-webdev, Kimi K3 scores 254.0 points higher — if your workload pattern matches that benchmark's task shape, the gap is meaningful.
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 GPT-5.4, switching to Kimi K3 means re-architecting that path (and vice versa).
- • Parallel tool calls
- • PDF input
- • Prompt caching
Capabilities both share (5)
- ✓ Function calling
- ✓ Vision input
- ✓ Streaming
- ✓ Structured output (JSON schema)
- ✓ Native reasoning mode
Benchmark winners — by the numbers
For each public benchmark that has scores for both models, the higher score and the size of the gap. Benchmarks are noisy — treat anything under a 2-point delta as effectively tied.
| Benchmark | GPT-5.4 | Kimi K3 | Winner | Δ |
|---|---|---|---|---|
| arena-elo | 1465.0 | 1488.0 | Kimi K3 | +23.0 |
| arena-elo-coding | 1511.0 | 1539.0 | Kimi K3 | +28.0 |
| arena-webdev | 1401.0 | 1655.0 | Kimi K3 | +254.0 |
| mmmu-pro | 81.2 | 81.6 | Kimi K3 | ~0 |
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Max output tokens differ: 128,000 on GPT-5.4 vs 131,072 on Kimi K3. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- GPT-5.4 has capabilities Kimi K3 lacks: Parallel tool calls, PDF input, Prompt caching. Switching to Kimi K3 means re-architecting any flow that depends on these.
- Provider changes from Azure AI Foundry to Fireworks 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 GPT-5.4 vs Kimi K3 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 GPT-5.4 primary, mirror 20% of traffic to Kimi K3 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 — GPT-5.4 vs Kimi K3
Which is cheaper, GPT-5.4 or Kimi K3? ▾
GPT-5.4 is cheaper by roughly 3% on a blended input + output token mix. Input prices are $2.50/M for GPT-5.4 versus $3.00/M for Kimi K3; output prices are $15.00/M versus $15.00/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 GPT-5.4 versus Kimi K3? ▾
GPT-5.4 supports up to 922,000 tokens of context. Kimi K3 supports up to 1,048,576 tokens. Kimi K3 has the larger window by a factor of 1.1x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
Do GPT-5.4 and Kimi K3 both support tool calling? ▾
Yes — both GPT-5.4 and Kimi K3 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? ▾
GPT-5.4 supports prompt caching; the other does not. If your agent has a stable system prompt + retrieval context block that repeats across requests, GPT-5.4 gives you a 50–90% discount on those repeated input tokens at the provider level.
When should I choose GPT-5.4 over Kimi K3? ▾
You re-send the same large system prompt across requests — GPT-5.4 supports prompt caching, cutting input cost on repeat hits.
When should I choose Kimi K3 over GPT-5.4? ▾
On arena-webdev, Kimi K3 scores 254.0 points higher — if your workload pattern matches that benchmark's task shape, the gap is meaningful.
How do I A/B test GPT-5.4 against Kimi K3 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.