DeepSeek R1 0528 vs Kimi K2.5
DeepSeek R1 0528 (2025) and Kimi K2.5 (2026) compare a standalone API model against a coding-specialized model. DeepSeek R1 0528 ships a 130k-token context window, while Kimi K2.5 ships a 256k-token context window. On MMLU PRO, Kimi K2.5 leads by 2.1 pts. On pricing, Kimi K2.5 costs $0.44/1M input tokens versus $0.50/1M for the alternative. This page treats the result as workflow and deployment fit, not a universal model winner.
Treat this as a product-type comparison: DeepSeek R1 0528 is standalone API model, while Kimi K2.5 is coding-specialized model. Choose based on workflow fit before reading any benchmark or price row as decisive.
Decision scorecard
Local evidence first| Signal | DeepSeek R1 0528 | Kimi K2.5 |
|---|---|---|
| Product type | Standalone API model | Coding-specialized model |
| Best for | reasoning-heavy apps and provider-routed production | custom coding agents, code generation, and tool loops |
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Agents |
| Context window | 130k | 256k |
| Cheapest output | $2.15/1M tokens | $2/1M tokens |
| Provider routes | 6 tracked | 10 tracked |
| Shared benchmarks | 5 rows | MMLU PRO leader |
Decision tradeoffs
- DeepSeek R1 0528 uniquely exposes Reasoning and Code execution in local model data.
- Local decision data tags DeepSeek R1 0528 for Coding, RAG, and Agents.
- Kimi K2.5 holds a shared-benchmark lead on MMLU PRO, ahead by 2.1 points.
- Kimi K2.5 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Kimi K2.5 has the lower cheapest tracked output price at $2/1M tokens.
- Kimi K2.5 has broader tracked provider coverage for fallback and procurement flexibility.
- Kimi K2.5 uniquely exposes Vision, Multimodal, and Function calling in local model data.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
DeepSeek R1 0528
$938
Cheapest tracked route/tier: OpenRouter
Kimi K2.5
$852
Cheapest tracked route/tier: OpenRouter
Estimated monthly gap: $85.50. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Fireworks AI, OpenRouter, and Together AI; start route-level A/B tests there.
- Kimi K2.5 is $0.15/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Reasoning and Code execution before moving production traffic.
- Kimi K2.5 adds Vision, Multimodal, and Function calling in local capability data.
- Provider overlap exists on Together AI, Fireworks AI, and Novita AI; start route-level A/B tests there.
- DeepSeek R1 0528 is $0.15/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Vision, Multimodal, and Function calling before moving production traffic.
- DeepSeek R1 0528 adds Reasoning and Code execution in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-05-28 | 2026-03-15 |
| Context window | 130k | 256k |
| Parameters | 685B total, 37B active (MoE) | 1T (MoE, 384 experts) |
| Architecture | decoder only | mixture of experts |
| License | MIT(OSI) | Proprietary |
| Openness | Open source | Proprietary |
| Commercial use | Commercial use allowed | Commercial use with conditions |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | DeepSeek R1 0528 | Kimi K2.5 |
|---|---|---|
| Input price | $0.50/1M tokens | $0.44/1M tokens |
| Output price | $2.15/1M tokens | $2/1M tokens |
| Providers |
Capabilities
| Capability | DeepSeek R1 0528 | Kimi K2.5 |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | Yes |
| Reasoning | Yes | No |
| Function calling | No | Yes |
| Tool use | No | No |
| Structured outputs | Yes | Yes |
| Code execution | Yes | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | DeepSeek R1 0528 | Kimi K2.5 |
|---|---|---|
| MMLU PRO | 85.0 | 87.1 |
| SWE-bench Verified | 57.6 | 76.8 |
| Google-Proof Q&A | 81.0 | 87.9 |
| AIME 2025 | 87.5 | 96.1 |
| LiveCodeBench | 73.3 | 85.0 |
Deep dive
On shared benchmark coverage, MMLU PRO has DeepSeek R1 0528 at 85 and Kimi K2.5 at 87.1, with Kimi K2.5 ahead by 2.1 points; SWE-bench Verified has DeepSeek R1 0528 at 57.6 and Kimi K2.5 at 76.8, with Kimi K2.5 ahead by 19.2 points; Google-Proof Q&A has DeepSeek R1 0528 at 81 and Kimi K2.5 at 87.9, with Kimi K2.5 ahead by 6.9 points. The largest visible gap is 19.2 points on SWE-bench Verified, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
The capability footprint differs most on vision: Kimi K2.5, multimodal input: Kimi K2.5, reasoning mode: DeepSeek R1 0528, function calling: Kimi K2.5, and code execution: DeepSeek R1 0528. Both models share structured outputs, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.
For cost, DeepSeek R1 0528 lists $0.50/1M input and $2.15/1M output tokens on the cheapest tracked provider, while Kimi K2.5 lists $0.44/1M input and $2/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Kimi K2.5 lower by about $0.09 per million blended tokens. Availability is 6 providers versus 10, so concentration risk also matters.
Choose DeepSeek R1 0528 when coding workflow support are central to the workload. Choose Kimi K2.5 when coding workflow support, larger context windows, and lower input-token cost are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.
FAQ
Which has a larger context window, DeepSeek R1 0528 or Kimi K2.5?
Kimi K2.5 supports 256k tokens, while DeepSeek R1 0528 supports 130k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, DeepSeek R1 0528 or Kimi K2.5?
Kimi K2.5 is cheaper on tracked token pricing. DeepSeek R1 0528 costs $0.50/1M input and $2.15/1M output tokens. Kimi K2.5 costs $0.44/1M input and $2/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is DeepSeek R1 0528 or Kimi K2.5 open source?
DeepSeek R1 0528 is listed under MIT. Kimi K2.5 is listed under Proprietary. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Which is better for vision, DeepSeek R1 0528 or Kimi K2.5?
Kimi K2.5 has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for multimodal input, DeepSeek R1 0528 or Kimi K2.5?
Kimi K2.5 has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run DeepSeek R1 0528 and Kimi K2.5?
DeepSeek R1 0528 is available on Together AI, Fireworks AI, GCP Vertex AI, Novita AI, and OpenRouter. Kimi K2.5 is available on Cloudflare Workers AI, Fireworks AI, OpenRouter, Together AI, and NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-06-04. Data sourced from public model cards and provider documentation.