Kimi K2.5 vs Grok 4.3

Pricing math on a real workload

Provider list price for Kimi K2.5 is $0.375/M input and $2.025/M output; on OpenKey that's $0.375 x 1.03 = $0.38625/M input and $2.025 x 1.03 = $2.08575/M output. Grok 4.3 lists at $1.25/M input and $2.50/M output, becoming $1.2875/M and $2.575/M on OpenKey after the same 3% fee. Run a 10M-input/2M-output job and Kimi K2.5 costs $7.80 versus Grok 4.3's $17.50 — Grok costs roughly 2.24x more for that workload. Grok 4.3 does offer cache-read pricing at $0.20/M, which Kimi K2.5 doesn't expose, so repeated-context workloads narrow the gap somewhat. Input price ratio between the two is 0.3 (Kimi K2.5 is 30% of Grok 4.3's input cost).

Coding and agent benchmarks

On design arena's shared categories, Kimi K2.5 outperforms Grok 4.3 across the board: godotgamedev (elo 1254, rank 2, 59.5% win rate for Kimi K2.5 vs elo 1134, rank 16, 38.6% for Grok 4.3), webapps (elo 1194 rank 15 vs elo 1194 rank 14 — a tie on elo but Kimi's win rate of 50.3% edges Grok's 46.6%), fullstack (elo 1182 rank 14, 54.2% vs elo 1072 rank 26, 31.4%), androidnative (elo 1132 rank 17, 57.8% vs elo 1074 rank 23, 28.6%), and mobileapps (elo 1186 rank 20, 49.3% vs elo 1146 rank 26, 39.1%). Grok 4.3 has artificial_analysis scores not available for Kimi K2.5: intelligence_index 37.6, coding_index 42.2, agentic_index 24.1 — useful as a standalone reference but not directly comparable here.

Context and long-document work

Grok 4.3 supports a 1,000,000-token context window against Kimi K2.5's 262,144 tokens — a context ratio of 0.26, meaning Kimi K2.5 holds about a quarter of what Grok 4.3 can. If your workload involves ingesting large codebases, long transcripts, or multi-document RAG in a single pass, Grok 4.3's window gives more headroom before you need chunking. Grok 4.3 also accepts file inputs directly (text+image+file), while Kimi K2.5 is limited to text+image. Neither model publishes a max_completion_tokens cap in this data, so output-length limits aren't a differentiator here.

Reasoning and tool support

Both models default to reasoning enabled and treat it as optional rather than mandatory. Grok 4.3 exposes explicit effort levels — high, medium, low, none — defaulting to low, which gives you a direct dial for cost vs. depth trade-offs on a per-request basis. Kimi K2.5 doesn't expose effort tiers but supports a broader parameter set overall, including min_p, top_k, and logit_bias, which Grok 4.3 doesn't list. Both support tool_choice, tools, structured_outputs, and response_format, so agentic tool-calling setups will work on either without rewriting your integration layer.

Questions

Which model is cheaper on OpenKey?

Kimi K2.5. Input runs $0.38625/M and output $2.08575/M on OpenKey (provider price x 1.03), versus Grok 4.3's $1.2875/M input and $2.575/M output. A 10M-input/2M-output job costs $7.80 on Kimi K2.5 versus $17.50 on Grok 4.3.

Which model has the bigger context window?

Grok 4.3, at 1,000,000 tokens versus Kimi K2.5's 262,144 tokens — a context ratio of 0.26. If you need to process very long documents or codebases in one call, Grok 4.3 gives more room before chunking is required.

Does either model support file uploads?

Grok 4.3 does — its modality is text+image+file->text. Kimi K2.5 supports text+image->text only, so PDF or raw file inputs need preprocessing before you send them to Kimi K2.5.

Which model wins on coding benchmarks?

Kimi K2.5, on every shared design-arena coding category: fullstack (elo 1182 vs 1072), androidnative (elo 1132 vs 1074), and godotgamedev (elo 1254, rank 2, vs elo 1134, rank 16). Grok 4.3's separate artificial_analysis coding_index is 42.2, but that metric isn't reported for Kimi K2.5 so it can't be compared directly.