[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-5-open-source-llms-coding-cost-en":3,"article-related-5-open-source-llms-coding-cost-en":35,"series-industry-82a5471b-135f-4828-9534-6a11428045a2":88},{"id":4,"slug":5,"title":6,"content":7,"summary":8,"source":9,"source_url":10,"author":11,"image_url":12,"cover_image":12,"category":13,"language":14,"translated_content":11,"related_article_id":15,"keywords":16,"key_takeaways":27,"views":31,"created_at":32,"published_at":33,"topic_cluster_id":34},"82a5471b-135f-4828-9534-6a11428045a2","5-open-source-llms-coding-cost-en","5 open source LLMs for coding and cost","\u003Cp data-speakable=\"summary\">This list ranks five \u003Ca href=\"\u002Fnews\u002Fmicrosoft-open-source-ai-safety-agent-tools-en\">open source\u003C\u002Fa> \u003Ca href=\"\u002Ftag\u002Fllms\">LLMs\u003C\u002Fa> by coding, reasoning, speed, context, and price.\u003C\u002Fp>\n\u003Cp>Updated in April 2026, this ranking helps you pick from five open-weight models using live benchmark data, including a top Quality Index of 56.584.\u003C\u002Fp>\n\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>Item\u003C\u002Fth>\u003Cth>Quality Index\u003C\u002Fth>\u003Cth>Best Price\u003C\u002Fth>\u003Cth>Top Speed\u003C\u002Fth>\u003Cth>Max Context\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd>Qwen3.7 Max\u003C\u002Ftd>\u003Ctd>56.584\u003C\u002Ftd>\u003Ctd>$3.75\u002FM\u003C\u002Ftd>\u003Ctd>202 tok\u002Fs\u003C\u002Ftd>\u003Ctd>991K\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Kimi K2.6\u003C\u002Ftd>\u003Ctd>53.905\u003C\u002Ftd>\u003Ctd>$1.44\u002FM\u003C\u002Ftd>\u003Ctd>327 tok\u002Fs\u003C\u002Ftd>\u003Ctd>262K\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>MiMo-V2.5-Pro\u003C\u002Ftd>\u003Ctd>53.829\u003C\u002Ftd>\u003Ctd>$1.20\u002FM\u003C\u002Ftd>\u003Ctd>88 tok\u002Fs\u003C\u002Ftd>\u003Ctd>1M\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>DeepSeek V4 Pro\u003C\u002Ftd>\u003Ctd>51.509\u003C\u002Ftd>\u003Ctd>$0.54\u002FM\u003C\u002Ftd>\u003Ctd>159 tok\u002Fs\u003C\u002Ftd>\u003Ctd>1M\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>MiniMax-M2.7\u003C\u002Ftd>\u003Ctd>49.615\u003C\u002Ftd>\u003Ctd>$0.52\u002FM\u003C\u002Ftd>\u003Ctd>446 tok\u002Fs\u003C\u002Ftd>\u003Ctd>205K\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\n\u003Ch2>1. [Qwen3.7 Max](https:\u002F\u002Fwhatllm.org\u002F) for best overall quality\u003C\u002Fh2>\n\u003Cp>Qwen3.7 Max leads the open source ranking with a Quality Index of 56.584, which makes it the safest first pick if you want \u003Ca href=\"\u002Fnews\u002Fminimax-m2-1-mixed-stack-coding-model-en\">one model\u003C\u002Fa> that can cover broad use cases without much guesswork. It also posts 202 tok\u002Fs and a 991K context window, so it is not just accurate, it is also practical for longer prompts.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1779667575913-lbu3.png\" alt=\"5 open source LLMs for coding and cost\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\n\u003Cul>\u003Cli>Best price: $3.75\u002FM\u003C\u002Fli>\u003Cli>Top speed: 202 tok\u002Fs\u003C\u002Fli>\u003Cli>Max context: 991K\u003C\u002Fli>\u003Cli>Use it when you want the strongest all-around open-weight model in the list\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>Its main tradeoff is cost. If you are optimizing for budget or very high throughput, other models on this list give you more room to spend elsewhere. But for teams that care most about quality, Qwen3.7 Max is the benchmark to beat.\u003C\u002Fp>\n\u003Ch2>2. [Kimi K2.6](https:\u002F\u002Fwhatllm.org\u002F) for speed and wide access\u003C\u002Fh2>\n\u003Cp>Kimi K2.6 sits near the top with a Quality Index of 53.905, but its bigger appeal is operational flexibility. It is the fastest model in this set at 327 tok\u002Fs and appears across 14 providers, which makes it easier to source and compare in real deployments.\u003C\u002Fp>\n\u003Cul>\u003Cli>Best price: $1.44\u002FM\u003C\u002Fli>\u003Cli>Top speed: 327 tok\u002Fs\u003C\u002Fli>\u003Cli>Max context: 262K\u003C\u002Fli>\u003Cli>Providers: 14\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>If you want an open-weight model that is easy to find and quick to respond, this is a strong choice. It is less attractive than Qwen3.7 Max for raw top-end quality, but it is better suited to apps where latency and provider choice matter more than absolute score.\u003C\u002Fp>\n\u003Ch2>3. [MiMo-V2.5-Pro](https:\u002F\u002Fwhatllm.org\u002F) for long documents\u003C\u002Fh2>\n\u003Cp>\u003Ca href=\"\u002Ftag\u002Fmimo\">MiMo\u003C\u002Fa>-V2.5-Pro is the context monster in this ranking, with a 1M \u003Ca href=\"\u002Ftag\u002Ftoken\">token\u003C\u002Fa> window that can handle very large briefs, logs, or multi-file code sessions. Its Quality Index of 53.829 keeps it close to the leaders, so you do not have to give up much performance to get that extra room.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1779667572026-4hfi.png\" alt=\"5 open source LLMs for coding and cost\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\n\u003Cul>\u003Cli>Best price: $1.20\u002FM\u003C\u002Fli>\u003Cli>Top speed: 88 tok\u002Fs\u003C\u002Fli>\u003Cli>Max context: 1M\u003C\u002Fli>\u003Cli>Good fit for document-heavy workflows\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>The tradeoff is speed. At 88 tok\u002Fs, it is not the quickest option here, so it is better for deep analysis than for chatty, high-volume traffic. Still, if your work lives in long prompts, this is one of the easiest models to justify.\u003C\u002Fp>\n\u003Ch2>4. [DeepSeek V4 Pro](https:\u002F\u002Fwhatllm.org\u002F) for low cost at scale\u003C\u002Fh2>\n\u003Cp>DeepSeek V4 Pro gives you a strong balance of quality and price, with a 51.509 Quality Index and a very low listed price of $0.54\u002FM. It also reaches 159 tok\u002Fs and supports a 1M context window, which makes it a flexible option for teams watching spend closely.\u003C\u002Fp>\n\u003Cul>\u003Cli>Best price: $0.54\u002FM\u003C\u002Fli>\u003Cli>Top speed: 159 tok\u002Fs\u003C\u002Fli>\u003Cli>Max context: 1M\u003C\u002Fli>\u003Cli>Good middle ground for production workloads\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>This is the model to watch if your priority is keeping \u003Ca href=\"\u002Ftag\u002Fapi\">API\u003C\u002Fa> costs down without dropping into the lowest tier of performance. It is not the top scorer, but it is one of the most practical picks for sustained usage, especially when \u003Ca href=\"\u002Ftag\u002Flong-context\">long context\u003C\u002Fa> matters.\u003C\u002Fp>\n\u003Ch2>5. [MiniMax-M2.7](https:\u002F\u002Fwhatllm.org\u002F) for the cheapest fast option\u003C\u002Fh2>\n\u003Cp>MiniMax-M2.7 is the budget speed pick, with the lowest listed price in this group at $0.52\u002FM and the highest top speed at 446 tok\u002Fs. Its Quality Index of 49.615 is lower than the leaders, but it still stays competitive enough for many everyday tasks.\u003C\u002Fp>\n\u003Cul>\u003Cli>Best price: $0.52\u002FM\u003C\u002Fli>\u003Cli>Top speed: 446 tok\u002Fs\u003C\u002Fli>\u003Cli>Max context: 205K\u003C\u002Fli>\u003Cli>Providers: 6\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>Choose this when response time and cost matter more than squeezing out the last few benchmark points. It is especially appealing for high-volume chat, routing, or lightweight assistant use where fast turnaround is the real win.\u003C\u002Fp>\n\u003Ch2>How to decide\u003C\u002Fh2>\n\u003Cp>If you want the best overall open source model, start with Qwen3.7 Max. If you care most about speed and provider choice, Kimi K2.6 is the cleaner fit. For long documents, MiMo-V2.5-Pro is the easiest recommendation, while DeepSeek V4 Pro and MiniMax-M2.7 are the best value picks for lower spend.\u003C\u002Fp>\n\u003Cp>For local or Ollama-style setups, hardware matters as much as model rank. In that case, use this list as a shortlist, then match the model to your VRAM, context needs, and tolerance for slower output.\u003C\u002Fp>","5 open source LLMs ranked for coding, reasoning, speed, context, and price, with live 2026 benchmark data.","whatllm.org","https:\u002F\u002Fwhatllm.org\u002Fbest-open-source-llm",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1779667575913-lbu3.png","industry","en","e642df22-7247-4581-91d5-0cec845a7269",[17,18,19,20,21,22,23,24,25,26],"open source LLM","best open source LLMs 2026","Qwen3.7 Max","Kimi K2.6","MiMo-V2.5-Pro","DeepSeek V4 Pro","MiniMax-M2.7","Ollama","self-hosted LLMs","LLM ranking",[28,29,30],"Qwen3.7 Max leads overall with the highest Quality Index at 56.584.","Kimi K2.6 is the fastest model here at 327 tok\u002Fs and has wide provider coverage.","MiMo-V2.5-Pro and DeepSeek V4 Pro are the long-context picks, while MiniMax-M2.7 is the cheapest fast option.",5,"2026-05-25T00:05:45.484425+00:00","2026-05-25T00:05:45.468+00:00","b43cd351-fc35-4ec1-8344-e955b617e1b4",{"tags":36,"relatedLang":47,"relatedPosts":51},[37,39,41,43,45],{"name":21,"slug":38},"mimo-v25-pro",{"name":18,"slug":40},"best-open-source-llms-2026",{"name":20,"slug":42},"kimi-k26",{"name":19,"slug":44},"qwen37-max",{"name":17,"slug":46},"open-source-llm",{"id":15,"slug":48,"title":49,"language":50},"5-open-source-llms-coding-cost-zh","5 個開源 LLM：寫程式與成本","zh",[52,58,64,70,76,82],{"id":53,"slug":54,"title":55,"cover_image":56,"image_url":56,"created_at":57,"category":13},"60f9f257-29a3-42fc-94a0-e781cae297a0","openai-ads-sensitive-chats-policy-en","OpenAI is right to keep ads out of sensitive chats","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781051570830-gx73.png","2026-06-10T00:32:23.894911+00:00",{"id":59,"slug":60,"title":61,"cover_image":62,"image_url":62,"created_at":63,"category":13},"4410b717-f1b6-4a96-854b-60dd47cc933e","ai-bootlegs-streaming-royalties-stick-figure-en","AI bootlegs are already draining streaming royalties","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781050678990-9idm.png","2026-06-10T00:17:31.471242+00:00",{"id":65,"slug":66,"title":67,"cover_image":68,"image_url":68,"created_at":69,"category":13},"317dc8b9-9ab1-4d29-8741-a50d795f7727","amd-microsoft-windows-ml-acceleration-en","AMD and Microsoft push Windows ML on GPU and NPU","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781047979576-a01a.png","2026-06-09T23:32:31.891479+00:00",{"id":71,"slug":72,"title":73,"cover_image":74,"image_url":74,"created_at":75,"category":13},"47702da7-3093-408a-90aa-9f5f461ccce9","openai-ipo-filing-turns-hype-into-scrutiny-en","OpenAI’s IPO filing turns hype into scrutiny","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781042611120-ynji.png","2026-06-09T22:03:05.09084+00:00",{"id":77,"slug":78,"title":79,"cover_image":80,"image_url":80,"created_at":81,"category":13},"619fab96-00b8-42f2-a3ff-13db32d6ac7b","skatteetaten-public-sector-ai-outcomes-en","Skatteetaten proves public sector AI should be judged by outcomes","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781038981764-h8ac.png","2026-06-09T21:02:32.623368+00:00",{"id":83,"slug":84,"title":85,"cover_image":86,"image_url":86,"created_at":87,"category":13},"45465fba-7f0e-4e19-979f-7902a8fc405a","openai-ipo-filing-wall-street-test-en","OpenAI’s IPO filing puts AI’s biggest test on Wall Street","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781032672165-bxm6.png","2026-06-09T19:17:23.738005+00:00",[89,94,99,104,109,114,119,124,129,134],{"id":90,"slug":91,"title":92,"created_at":93},"d35a1bd9-e709-412e-a2df-392df1dc572a","ai-impact-2026-developments-market-en","AI's Impact in 2026: Key Developments and Market Shifts","2026-03-25T16:20:33.205823+00:00",{"id":95,"slug":96,"title":97,"created_at":98},"5ed27921-5fd6-492e-8c59-78393bf37710","trumps-ai-legislative-framework-en","Trump's AI Legislative Framework: What's Inside?","2026-03-25T16:22:20.005325+00:00",{"id":100,"slug":101,"title":102,"created_at":103},"e454a642-f03c-4794-b185-5f651aebbaca","nvidia-gtc-2026-key-highlights-innovations-en","NVIDIA GTC 2026: Key Highlights and Innovations","2026-03-25T16:22:47.882615+00:00",{"id":105,"slug":106,"title":107,"created_at":108},"0ebb5b16-774a-4922-945d-5f2ce1df5a6d","claude-usage-diversifies-learning-curves-en","Claude Usage Diversifies, Learning Curves Emerge","2026-03-25T16:25:50.770376+00:00",{"id":110,"slug":111,"title":112,"created_at":113},"69934e86-2fc5-4280-8223-7b917a48ace8","openclaw-ai-commoditization-concerns-en","OpenClaw's Rise Raises Concerns of AI Model Commoditization","2026-03-25T16:26:30.582047+00:00",{"id":115,"slug":116,"title":117,"created_at":118},"b4b2575b-2ac8-46b2-b90e-ab1d7c060797","google-gemini-ai-rollout-2026-en","Google's Gemini AI Rollout Extended to 2026","2026-03-25T16:28:14.808842+00:00",{"id":120,"slug":121,"title":122,"created_at":123},"6e18bc65-42ae-4ad0-b564-67d7f66b979e","meta-llama4-fabricated-results-scandal-en","Meta's Llama 4 Scandal: Fabricated AI Test Results Unveiled","2026-03-25T16:29:15.482836+00:00",{"id":125,"slug":126,"title":127,"created_at":128},"bf888e9d-08be-4f47-996c-7b24b5ab3500","accenture-mistral-ai-deployment-en","Accenture and Mistral AI Team Up for AI Deployment","2026-03-25T16:31:01.894655+00:00",{"id":130,"slug":131,"title":132,"created_at":133},"5382b536-fad2-49c6-ac85-9eb2bae49f35","mistral-ai-high-stakes-2026-en","Mistral AI: Facing High Stakes in 2026","2026-03-25T16:31:39.941974+00:00",{"id":135,"slug":136,"title":137,"created_at":138},"9da3d2d6-b669-4971-ba1d-17fdb3548ed5","cursors-meteoric-rise-pressures-en","Cursor's Meteoric Rise Faces Industry Pressures","2026-03-25T16:32:21.899217+00:00"]