API cost decision in 10 seconds

Relace Search vs Kimi K2 Thinking

Pick Kimi K2 Thinking when budget and context both matter.

Pricing data updated: 2026-05-26T11:08:00Z Prices normalized to USD per 1M tokens Sample workload: 1M input + 500K output

Budget verdict

Pick Kimi K2 Thinking when budget and context both matter.

On the standard 1M input plus 500K output workload, Kimi K2 Thinking is estimated at $1.85 vs $2.5 for Relace Search, saving $0.65 (26% lower).

Cost-first pickKimi K2 Thinking

Context-first pickKimi K2 Thinking

Sample savings$0.6526%

10x traffic gap$6.5

Kimi K2 Thinking is cheaper on the standard workload and also has the larger context window. At 10x that traffic, the same price gap is about $6.5. Use the calculator below to replace the sample workload with your own token volume.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

Kimi K2 Thinking stays cheaper across input-heavy, balanced, and output-heavy sample workloads.

Workload shape	Token mix	Better pick	Relace Search	Kimi K2 Thinking
Input-heavy / RAG	5M input + 500K output	Kimi K2 Thinking	$6.5	$4.25
Balanced workload	1M input + 1M output	Kimi K2 Thinking	$4	$3.1
Output-heavy chatbot	1M input + 5M output	Kimi K2 Thinking	$16	$13.1

Cheaper input Kimi K2 Thinking $1 vs $0.6 / 1M

Kimi K2 Thinking is $0.4 cheaper per 1M input tokens (40% lower; 1.67x difference).

Cheaper output Kimi K2 Thinking $3 vs $2.5 / 1M

Kimi K2 Thinking is $0.5 cheaper per 1M output tokens (16.7% lower; 1.2x difference).

Larger context Kimi K2 Thinking 256K vs 262.14K

Kimi K2 Thinking has 6.14K more context (1.02x larger).

Sample workload Kimi K2 Thinking $2.5 vs $1.85

Kimi K2 Thinking is $0.65 cheaper on the standard workload (26% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.

Monthly input tokens Monthly output tokens

Relace Search Calculating… Estimated API cost

Kimi K2 Thinking Calculating… Estimated API cost

Cheaper for this workload Calculating… Difference: calculating…

This estimate uses normalized public API pricing per 1M tokens. It is a planning aid, not a billing quote. Verify provider pricing, limits, and terms before production use.

Quick Decision

Verdict

Kimi K2 Thinking has the lower input price; Kimi K2 Thinking has the lower output price; Kimi K2 Thinking offers the larger context window. For the 1M input plus 500K output sample, Kimi K2 Thinking is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $2.5 for Relace Search and $1.85 for Kimi K2 Thinking.

Best Fit

Choose Relace Search when its provider, model quality, latency, or availability is more important than the numeric price/context winner.

Choose Kimi K2 Thinking when you care most about lower input-token price, lower output-token price, and larger context window.

Decision Notes

On the standard 1M input plus 500K output workload, Kimi K2 Thinking is estimated at $1.85 vs $2.5 for Relace Search, saving $0.65 (26% lower).
Kimi K2 Thinking is $0.65 cheaper on the standard workload (26% lower).
Kimi K2 Thinking is $0.4 cheaper per 1M input tokens (40% lower; 1.67x difference).
Kimi K2 Thinking is $0.5 cheaper per 1M output tokens (16.7% lower; 1.2x difference).
Kimi K2 Thinking has 6.14K more context (1.02x larger).

Head-to-Head Specs

Feature	Relace Search (Relace)	Kimi K2 Thinking (MoonshotAI)
Input Price prompt tokens per 1M	$1	$0.6
Completion Price per 1M tokens	$3	$2.5
Sample Workload Cost 1M input + 500K output	$2.5	$1.85
Context Window	256K	262.14K
Release Date	2025-12-08	2025-11-06

Use-Case Decision Matrix

Use case	Better pick	Why
Budget-constrained production	Kimi K2 Thinking	On the standard 1M input plus 500K output workload, Kimi K2 Thinking is estimated at $1.85 vs $2.5 for Relace Search, saving $0.65 (26% lower).
High-volume input processing	Kimi K2 Thinking	Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbots	Kimi K2 Thinking	Lower output-token price matters most when assistants generate many completion tokens.
RAG or long-document work	Kimi K2 Thinking	A larger context window leaves more room for retrieved passages, conversation history, or source files.

Related Alternatives

Same-provider lower-cost swaps

Relace Apply 3 can replace Relace Search when lower sample workload cost matters most: $1.48.
Kimi K2.5 can replace Kimi K2 Thinking when lower sample workload cost matters most: $1.35.
Kimi K2 0711 can replace Kimi K2 Thinking when lower sample workload cost matters most: $1.72.

Larger context near this budget

Llama 4 Scout offers 10M context with $0.23 sample workload cost.
Grok 4.20 offers 2M context with $2.5 sample workload cost.
Owl Alpha offers 1.05M context with $0 sample workload cost.
Gemini 3.1 Flash Lite offers 1.05M context with $1 sample workload cost.

Popular competitors

No popular competitor is currently available.

Cheaper alternatives

Review low-cost models sorted by a standard 1M input plus 500K output workload.

Open cheapest models

Larger context alternatives

Find models with larger context windows for RAG, long documents, and codebase review.

Open largest context models

Provider catalogs

Compare models within provider hubs before choosing a final API vendor.

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Relace catalog

Review all tracked Relace models before deciding whether this matchup is the right shortlist.

Open Relace models

MoonshotAI catalog

Check other MoonshotAI models with comparable pricing, context, or release timing.

Open MoonshotAI models

Relace Search

The relace-search model uses 4-12 `view_file` and `grep` tools in parallel to explore a codebase and return relevant files to the user request. In contrast to RAG, relace-search performs agentic...

Kimi K2 Thinking

Kimi K2 Thinking is Moonshot AI’s most advanced open reasoning model to date, extending the K2 series into agentic, long-horizon reasoning. Built on the trillion-parameter Mixture-of-Experts (MoE) architecture introduced in...