GLM-5.2 vs Kimi K2.6/K2.7
GLM-5.2 vs Kimi K2.6 and Kimi K2.7 Code for cheap coding models, with Kimi K3 separated as the newer flagship lane.
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Compare active GPT-6 and Opus 5.5 routes, value models, restricted releases, and historical evidence by access, workload, and accepted-result cost.
Compare AI models by price-to-capability ratio. Three tiers, clear tradeoffs, no marketing fluff.
Price: $0.25–$1.00 per million input tokens
Best for: Prototyping, preprocessing, hobby projects, high-volume workflows
Models:
Bottom line: Compare cost per successful task. Do not turn scores from different benchmark variants into one percentage of “frontier performance.”
Price: $1.00–$3.00 per million input tokens
Best for: Production apps, daily coding, reliable reasoning
Models and lanes to compare:
Bottom line: This is the production spend band. Use it for daily work, then escalate only when the task proves it needs a premium model.
Price: $5.00+ per million input tokens Best for: Complex research, enterprise workloads, premium arbitration, and tasks where a better answer is worth the bill
Models:
OpenAI family: GPT-6 Sol and Luna are the current hard-work and value routes in this comparison; GPT-6 Astra has a separate planning evaluation. Keep current access and price claims on their owner pages.
Current price anchors are $4/$20 for Opus 5.5, $2/$10 for GPT-6 Sol, and $0.10/$0.50 for GPT-6 Luna, with cache-read rates owned by the linked guides. Verify context tiers, plan access, and checkout before purchase.
Bottom line: Do not promote premium as a default. Measure fallback/refusal behavior, cached-input economics, retention requirements, and cost per successful task.
GPT-5.5, GPT-5.6, and Opus 5 remain useful for pinned integrations and dated benchmark comparisons. Their prices and scores belong to the historical record; they do not define the current premium recommendation.
| Your Constraint | Recommended Tier | Why |
|---|---|---|
| Cost is everything | Budget | Process millions of tokens for dollars |
| Production reliability | Mid-range | Best balance of capability and cost |
| Premium arbitration | Premium | Use an active premium model only when it changes the result |
| Newest announced models | Model status guide | Separate preview and restricted access from active availability |
Pricing: Current official list prices, with subscriptions and API rates kept separate.
Benchmarks: Exact benchmark names and variants, with independent and vendor evidence labeled separately.
Use cases: Provider specifications plus explicit local-evaluation gaps; no implied hands-on result without an artifact.
See /verify/methodology/ for full verification standards.
For security-task caveats, see the LLM app-hacking field test. It separates generic model price/performance from cost per confirmed exploit on one deliberately vulnerable app.
GPT-6 and Opus 5.5 pointers were refreshed September 27, 2026. Other provider entries retain their source dates; pricing, access, open-weight status, and benchmark positions are subject to change.
GLM-5.2 vs Kimi K2.6 and Kimi K2.7 Code for cheap coding models, with Kimi K3 separated as the newer flagship lane.
Shortlist MiMo V2.6 Flash, DeepSeek V4.1 Flash, GLM-5.3 and Luna for useful low-cost work, with dated evidence and an accepted-result checklist.
Current mid-range model-routing guide: GLM-5.2, Kimi K3, Kimi K2.7 Code, MiniMax M3, Claude Sonnet 5, GPT-6 Luna, and deliberate premium escalation.
Compare Sonnet 5, Opus 5.5, GPT-6, Fable 5, and restricted models by access, evidence, practical role, and cost per accepted task.