Accounts Fireworks Models Kimi K2p7 Code vs Grok 3 mini Fast latest
Accounts Fireworks Models Kimi K2p7 Code (Fireworks AI, 262,144-token context) versus Grok 3 mini Fast latest (xAI, 131,072-token context). Grok 3 mini Fast latest is cheaper by 7% on a blended token mix. Accounts Fireworks Models Kimi K2p7 Code uniquely supports vision input and structured output (json schema). Grok 3 mini Fast latest uniquely supports prompt caching. 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 Kimi K2p7 Code vs Grok 3 mini Fast latest
Accounts Fireworks Models Kimi K2p7 Code and Grok 3 mini Fast latest are priced within 7% 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.
Accounts Fireworks Models Kimi K2p7 Code ships a 262,144-token context window, 2.0x larger than Grok 3 mini Fast latest's 131,072 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 131,072 tokens, the extra context on Accounts Fireworks Models Kimi K2p7 Code is insurance you may never use — and Grok 3 mini Fast latest may win on other axes.
On capability surface area, the models diverge: Accounts Fireworks Models Kimi K2p7 Code supports vision input where the other does not; Accounts Fireworks Models Kimi K2p7 Code supports structured output (json schema) where the other does not; Grok 3 mini Fast latest 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: grok-3-mini-fast-latest
provider: xai
fallback:
model: accounts-fireworks-models-kimi-k2p7-code
provider: fireworks-ai
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| Accounts Fireworks Models Kimi K2p7 Code | Grok 3 mini Fast latest | |
|---|---|---|
| Input price | $0.950/M | $0.600/M |
| Output price | $4.00/M | $4.00/M |
| Context window | 262,144 | 131,072 |
| Max output | 32,768 | 131,072 |
| 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 Kimi K2p7 Code | Grok 3 mini Fast latest | Delta |
|---|---|---|---|
| Startup 10K requests/day | $525 /mo | $420 /mo | $105/mo |
| Mid-market 100K requests/day | $5,250 /mo | $4,200 /mo | $1,050/mo |
| Enterprise 1M requests/day | $52,500 /mo | $42,000 /mo | $10,500/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 workload needs long context — Accounts Fireworks Models Kimi K2p7 Code fits 262,144 tokens versus the other model's 131,072, enough headroom for full books, large codebases, or 100+ page documents in one shot.
Your inputs include screenshots, diagrams, or product photos — Accounts Fireworks Models Kimi K2p7 Code accepts image input natively, the other doesn't.
You re-send the same large system prompt across requests — Grok 3 mini Fast latest 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 Accounts Fireworks Models Kimi K2p7 Code, switching to Grok 3 mini Fast latest means re-architecting that path (and vice versa).
- • Vision input
- • Structured output (JSON schema)
- • Prompt caching
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 down 50% when moving from Accounts Fireworks Models Kimi K2p7 Code (262,144) to Grok 3 mini Fast latest (131,072). Re-check any prompt that relies on cramming long history or documents.
- Max output tokens differ: 32,768 on Accounts Fireworks Models Kimi K2p7 Code vs 131,072 on Grok 3 mini Fast latest. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- Accounts Fireworks Models Kimi K2p7 Code has capabilities Grok 3 mini Fast latest lacks: Vision input, Structured output (JSON schema). Switching to Grok 3 mini Fast latest means re-architecting any flow that depends on these.
- Grok 3 mini Fast latest has capabilities Accounts Fireworks Models Kimi K2p7 Code lacks: Prompt caching. Worth wiring through the agent design before commit.
- Provider changes from Fireworks AI to xAI. 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 Kimi K2p7 Code vs Grok 3 mini Fast latest 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 Kimi K2p7 Code primary, mirror 20% of traffic to Grok 3 mini Fast latest 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 Kimi K2p7 Code vs Grok 3 mini Fast latest
Which is cheaper, Accounts Fireworks Models Kimi K2p7 Code or Grok 3 mini Fast latest? ▾
Grok 3 mini Fast latest is cheaper by roughly 7% on a blended input + output token mix. Input prices are $0.950/M for Accounts Fireworks Models Kimi K2p7 Code versus $0.600/M for Grok 3 mini Fast latest; output prices are $4.00/M versus $4.00/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 Accounts Fireworks Models Kimi K2p7 Code versus Grok 3 mini Fast latest? ▾
Accounts Fireworks Models Kimi K2p7 Code supports up to 262,144 tokens of context. Grok 3 mini Fast latest supports up to 131,072 tokens. Accounts Fireworks Models Kimi K2p7 Code has the larger window by a factor of 2.0x, 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 Kimi K2p7 Code and Grok 3 mini Fast latest both support tool calling? ▾
Yes — both Accounts Fireworks Models Kimi K2p7 Code and Grok 3 mini Fast latest 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 Kimi K2p7 Code and Grok 3 mini Fast latest process images? ▾
Accounts Fireworks Models Kimi K2p7 Code accepts native image input. Grok 3 mini Fast latest does not — you would need to route image-heavy workloads through Accounts Fireworks Models Kimi K2p7 Code or add a separate vision model in front of Grok 3 mini Fast latest.
Which model supports prompt caching for cost reduction? ▾
Grok 3 mini Fast latest supports prompt caching; the other does not. If your agent has a stable system prompt + retrieval context block that repeats across requests, Grok 3 mini Fast latest gives you a 50–90% discount on those repeated input tokens at the provider level.
When should I choose Accounts Fireworks Models Kimi K2p7 Code over Grok 3 mini Fast latest? ▾
Your workload needs long context — Accounts Fireworks Models Kimi K2p7 Code fits 262,144 tokens versus the other model's 131,072, enough headroom for full books, large codebases, or 100+ page documents in one shot. Your inputs include screenshots, diagrams, or product photos — Accounts Fireworks Models Kimi K2p7 Code accepts image input natively, the other doesn't.
When should I choose Grok 3 mini Fast latest over Accounts Fireworks Models Kimi K2p7 Code? ▾
You re-send the same large system prompt across requests — Grok 3 mini Fast latest supports prompt caching, cutting input cost on repeat hits.
How do I A/B test Accounts Fireworks Models Kimi K2p7 Code against Grok 3 mini Fast latest 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.