How to Pick the Best LLM for Programming Cost vs Capability in 2025

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In 2025, dozens of language models can now write, debug, or refactor code. But which ones are actually good — and which give the best value for money? Let's try to make a quick recap for your future programming session.

Disclaimer: this is a moving market and each day can see tremendous changes so this is a picture in time and a methodology example for your own review 

🧱 How We Calculate Token Costs

The 80 / 20 blended formula

Most IDE copilots send a large prompt (project context, previous messages, code files) and receive a short answer (patch, explanation). Field logs from Cline, GitHub Copilot, Cursor and Sourcegraph Cody show that roughly 80 % of tokens are input and 20 % are output.

To give a single price you can reason about, we compute:

Blended Cost ($/M) = 0.80 × Price_in  +  0.20 × Price_out

Example: Claude 4 Opus costs $15 /M input and $75 /M output → 0.80×15 + 0.20×75 ≈ $27 /M blended.

Price sources: latest vendor docs (OpenAI, Anthropic, Google Vertex, Mistral) and live rates on OpenRouter.ai. OpenRouter often undercuts vendor direct prices by pooling GPUs and batching traffic.

Why we use SWE‑bench Verified as the reference benchmark

SWE‑bench asks the model to repair 500 real GitHub issues end‑to‑end: understand the bug, edit multiple files, run unit tests, and produce a passing PR. It is harder than snippet benchmarks like HumanEval because it measures:

  1. Long‑context reasoning – several files and test outputs at once.
  2. Planning & tool use – the agent must iterate, not just dump code.
  3. Practical correlation – scores track well with how much a copilot actually helps in real projects.

The Verified variant re‑runs every patch against the original test suite, eliminating false positives. That makes it a solid yard‑stick for build‑pipeline or CI usage.

📊 Cost vs SWE‑bench Verified Performance

(All prices from OpenRouter; rounded to nearest cent unless noted)

ModelCost / 1 M tok.SWE‑bench VerifiedMCP CompatibleNotes
Devstral-Small 24B (open)$0.0846.8 %✅ YesCheapest open-weight with solid bug-fix power (OpenRouter).
GPT-4o Mini$0.2426 %✅ YesOpenAI’s own benchmark.
Gemini 2.5 Flash$0.24~55 %2⚠ PartialUnofficial community scores only.
LLaMA 3 70B Instruct (open)$0.3241 %1❌ NoRL-tuned variant recently evaluated.
DeepSeek-Chat V3 (open)$0.4442 %✅ YesFree locally, inexpensive via API.
DeepSeek-Reasoner R1$0.8849.2 %✅ YesAdds chain-of-thought, closes half the gap with Flash.
Gemini 2.5 Pro$3.0063.8 %⚠ PartialBest sub‑$10 premium coder.
Claude 4 Sonnet$5.4072.7 %✅ YesSweet‑spot of cost vs accuracy.
Claude 4 Opus$27.0072.5 %✅ YesFlagship quality, premium price.

1 RL-tuned variant of Meta’s LLaMA 3 70B evaluated on SWE-bench Verified (May 2025). 

2 Flash performance based on community scaffold (OpenHands); Google has not published official SWE-bench Verified results yet.

3 MCP (Model Context Protocol) is an open protocol that allows LLMs to interface with external tools, preserve execution context, and behave like autonomous agents. It’s used in advanced IDE plugins, agent frameworks, and AI development assistants.

💸 Monthly Projection for Copilot Use (2 h/day)

For realistic usage projections, we assume 2 hours of coding per day (∼60 hours/month), with copilots consuming about 50k to 150k tokens/hour depending on their design. Tools like Cline can be especially intensive due to:

  • Resending full file context and prior messages every step.
  • Automatically chaining planning, testing, fixing steps.
  • Using memory banks or embedding context that expands requests.
Usage levelTokens/monthDeepSeek-Chat V3Gemini 2.5 ProClaude 4 Opus
Light (chat/autocomplete)0.6M$0.26$1.80$16.20
Medium (structured copilot)3M$1.32$9.00$81.00
Heavy (agentic / vibecoding)9M$3.96$27.00$243.00

⚠ Tip: Experiences with Cline show that the output token count can go really high. Consider enabling diff mode and disabling full memory context to reduce token usage and costs.

🔍 Track Live Popularity

OpenRouter publishes a Programming Leaderboard based on real token traffic: https://openrouter.ai/rankings/programming?view=week — stats reset every Monday UTC.

It’s a fast way to see which models developers actually trust each week.

📚 Appendix — Other Leaderboards

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