Kimi K2 0905 vs Relace Apply 3
Relace Apply 3 costs 2.0x less per output token than Kimi K2 0905 ($1.25 vs $2.50 per million). Updated October 21, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $2.50
- Coding
- No data
- Overall
- No data
- Output $/M
- $1.25
Kimi K2 0905 or Relace Apply 3: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Kimi K2 0905 for a larger context window (262K against 256K tokens).
No benchmark has tested both yet. Kimi K2 0905 has results on LiveCodeBench (Artificial Analysis run), Terminal-Bench Hard (Artificial Analysis run) and WeirdML; Relace Apply 3 has none yet. Compare them on price and context instead.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only Kimi K2 0905 has a AA Intelligence Index result, 15.3.
Directional only
Web design
Design Arena: websites
No call
Only Kimi K2 0905 has a Design Arena: websites result, 1089.
Directional only
Long documents
Listed context window
Kimi K2 0905
Kimi K2 0905 takes 262K tokens against 256K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Relace Apply 3
Kimi K2 0905 $0.0019, Relace Apply 3 $0.0015 per turn.
List prices
At a glance
- Coding index
- Kimi K2 0905 not benchmarked, Relace Apply 3 not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- Kimi K2 0905 $2.50 against Relace Apply 3 $1.25 per million output tokens, 2.0x apart.
- Context window
- Kimi K2 0905 takes 262K tokens, Relace Apply 3 256K.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index4 boards measured
| Benchmark | Kimi K2 0905 | Relace Apply 3 | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | 61.0% | Not measured | Only Kimi K2 0905 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 23.5% | Not measured | Only Kimi K2 0905 measured |
| WeirdML | 36.7% | Not measured | Only Kimi K2 0905 measured |
| ALE-Bench | 267.1 | Not measured | Only Kimi K2 0905 measured |
| Neither model has a result on the other 35 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Kimi K2 0905 | Relace Apply 3 | Leader |
|---|---|---|---|
| AA Intelligence Index | 15.3 | Not measured | Only Kimi K2 0905 measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Kimi K2 0905 | Relace Apply 3 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 73.4% | Not measured | Only Kimi K2 0905 measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | Kimi K2 0905 | Relace Apply 3 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 76.7% | Not measured | Only Kimi K2 0905 measured |
| Humanity's Last Exam (Artificial Analysis run) | 6.4% | Not measured | Only Kimi K2 0905 measured |
| Kagi LLM Benchmark | 52.2% | Not measured | Only Kimi K2 0905 measured |
| Neither model has a result on the other 19 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Kimi K2 0905 | Relace Apply 3 | Leader |
|---|---|---|---|
| AA Math Index | 57.3 | Not measured | Only Kimi K2 0905 measured |
| AIME 2025 (Artificial Analysis run) | 57.3% | Not measured | Only Kimi K2 0905 measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Kimi K2 0905 | Relace Apply 3 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 81.9% | Not measured | Only Kimi K2 0905 measured |
| Neither model has a result on the other 17 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Kimi K2 0905 | Relace Apply 3 | Leader |
|---|---|---|---|
| Design Arena: websites | 1089 | Not measured | Only Kimi K2 0905 measured |
| Design Arena: all code categories | 1098 | Not measured | Only Kimi K2 0905 measured |
| Neither model has a result on the other 7 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | Kimi K2 0905 | Relace Apply 3 | Leader |
|---|---|---|---|
| IFBench | 41.7% | Not measured | Only Kimi K2 0905 measured |
| Long context reasoning (AA-LCR) | 53.0% | Not measured | Only Kimi K2 0905 measured |
| Fiction.LiveBench | 66.7% | Not measured | Only Kimi K2 0905 measured |
| Neither model has a result on the other 43 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Relace Apply 3 costs less
Repository review
50K input + 3K output tokens
Kimi K2 0905 costs less
Agent loop
200K input + 10K output tokens
Kimi K2 0905 costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | MoonshotAI Kimi K2 0905 | Relace Apply 3 |
|---|---|---|
| Maker | Moonshot AI | Relace |
| Weights | Open weights | No public weights listed |
| Released | September 4, 2025 | September 26, 2025 |
| Context window | 262K | 256K |
| Input $/Mtok | $0.60 | $0.85 |
| Output $/Mtok | $2.50 | $1.25 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Kimi K2 0905 better than Relace Apply 3 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Kimi K2 0905 for a larger context window (262K against 256K tokens).
Which is cheaper, Kimi K2 0905 or Relace Apply 3?
Kimi K2 0905 costs $0.60 per million input tokens and $2.50 per million output tokens; Relace Apply 3 costs $0.85 and $1.25. Cost per solved task is known for neither of them.
Which has the bigger context window, Kimi K2 0905 or Relace Apply 3?
Kimi K2 0905, with 262K tokens against 256K.
Has any benchmark tested both Kimi K2 0905 and Relace Apply 3?
No benchmark has tested both yet. Kimi K2 0905 has results on LiveCodeBench (Artificial Analysis run), Terminal-Bench Hard (Artificial Analysis run) and WeirdML; Relace Apply 3 has none yet. Compare them on price and context instead. Data as of October 21, 2026.