A benchmark study demonstrates that using LLMs to analyze entire codebases is cost-effective, identifying DeepSeek V4 Flash as the optimal default model due to its low cost and comparable accuracy to premium options like Claude Opus.
# For providing better context to AI Copilots . ### We use LLMs to analyze every file in your codebase. ### Result is 80% less cost and at least 10% accuracy increase. ### However This seems a stupid idea because of cost. ### Yet LLMs are far, far better for code analysis than vectors or AST parsers, and the math works out fine once you pick the right model. ### The benchmark across 14 models on 30 kubernetes ecosystem files settled it. # What the benchmark actually shows We ran 14 models through 30 files across 7 weighted categories (search, graph, semantic, integration, section map, business context, JSON). After applying a quality floor of 70 weighted accuracy, two models dropped out: Stepfun Step 3.5 Flash at 69.71 and GPT 5.4 at 55.65. The remaining 12 models, sorted by cost to ingest 1000 files, look like this: Model $/1k files Score ───────────────────────────────────────────── deepseek-v4-flash $0.75 71.13 mimo-v2.5 $1.10 71.10 minimax-m2.7 $1.37 70.61 glm-5.1 $1.46 72.22 kimi-latest $1.61 72.29 qwen3.6-plus $2.11 71.40 deepseek-v4-pro $3.00 71.98 qwen3.6-max-preview $3.25 72.28 claude-sonnet-4.6 $8.13 73.56 grok-4.3 $13.48 72.10 claude-opus-4.6 $39.86 73.67 claude-opus-4.7 $41.88 73.43 The outcome is striking once you stare at it for a minute. The cheapest qualifying model (DeepSeek V4 Flash at 75 cents per 1000 files) and the most expensive (Claude Opus 4.7 at $41.88) are separated by **56× in cost** but only **2.3 points in accuracy**. That is the entire story right there. DeepSeek V4 Flash, MiMo V2.5, MiniMax M2.7, GLM 5.1, and Kimi Latest all sit in the $0.75 to $1.61 range with accuracy between 70.61 and 72.29. Any of them is a sensible default for bulk ingestion. Move up to Sonnet 4.6 and you pay 5× to 10× more for a 1 to 2 point accuracy bump, which is worth it for a premium tier but not for default ingestion. Move up to Opus and you pay 25× to 55× more for accuracy that is statistically indistinguishable from Sonnet, which is hard to justify for any ingestion workload. Grok 4.3 is the odd one out. It costs $13.48 per 1000 files, sits between Sonnet and Opus on price, but scores 72.10, which is lower than models costing one tenth as much. There is no workload where Grok is the right answer. The two disqualified models are also worth a note. Stepfun is the cheapest model on the entire board at 56 cents per 1000 files but misses the 70 point quality floor by 0.29 points. For non-production analysis or exploration work, it might still be a fine choice. GPT 5.4 costs $23.39 per 1000 files and scores 55.65, which means it is both expensive and significantly less accurate than every alternative. Worth flagging that this looks like it might be a configuration issue with our eval setup rather than a real model problem, because the gap is large enough to be suspicious. The bottom line in numbers: **DeepSeek V4 Flash for default ingestion at 75 cents per 1000 files, GLM 5.1 for the balanced tier at $1.46, Sonnet 4.6 for premium at $8.13.** Opus is not on this list because nothing about its accuracy profile justifies a 25× to 55× cost premium for indexing work.
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