Openai/691a14bb-ac48-8008-85d0-2e4fb3f780d1

This conversation was exported from ChatGPT

gpt-5

User: Attached is a screenshot showing the current list of available models accessible within Windsurf for use with the Casca…

Attached is a screenshot showing the current list of available models accessible within Windsurf for use with the Cascade coding agent. I have access to the Pro plan in Windsurf, so I can use any of the models shown here (except those that indicate "BYOK").

I've primarily been using "GPT-5 high reasoning" and "GPT-5 medium reasoning". But, I'm trying to determine which of the other models in the list might be a better choice.

Tell me the pros and cons of the available models, and in what coding situations/scenarios each model would be most appropriate and advantageous.

Assistant: Understood.

Understood. Here’s a model-by-model breakdown of the options shown in your Windsurf screenshot, focused strictly on coding performance, reasoning, and optimal use cases:

GPT-5 (low / medium / high reasoning)

• Pros: - Best at structured reasoning, complex refactoring, and long-context comprehension. - High-reasoning variant excels in debugging multi-file codebases and explaining architectural decisions. - Medium-reasoning offers nearly equal code accuracy at lower credit cost.
• Cons: - Slower for small edits or single-function tasks.
• Use: - High: full-system analysis, architectural planning, deep debugging. - Medium: mid-complex refactors, writing new modules. - Low: quick utility generation, single-function edits.

GPT-5-Codex

• Pros: Specialized for pure code completion, boilerplate generation, and syntax precision.
• Cons: Weak reasoning; not ideal for architectural design or explanation.
• Use: Code generation, scaffolding, template expansion.

GPT-4o / GPT-4.1

• Pros: Fast, balanced generalist. Reliable language-code interleaving.
• Cons: Narrower context, weaker long-term reasoning than GPT-5.
• Use: Documentation writing, lightweight code reviews, mixed natural-language and code tasks.

gpt-oss 120B (Medium)

• Pros: Good for rapid prototyping and experimentation.
• Cons: Inconsistent accuracy, occasional hallucinations.
• Use: Sandbox code sketches or exploratory tasks.

Claude Opus 4.1 / Thinking

• Pros: Exceptional long-context comprehension, strong safety and reasoning discipline.
• Cons: Very slow, high credit cost.
• Use: Large-file reasoning, dependency mapping, or multi-repo audits.

Claude Sonnet 4.5 / Thinking

• Pros: Improved speed-to-accuracy ratio over Opus. Handles multi-file code contexts well.
• Cons: Slightly verbose; may under-specify low-level code.
• Use: Design discussions, complex refactors needing explanation.

Claude Haiku 4.5

• Pros: Very fast, low-credit.
• Cons: Simplified reasoning, weaker on subtle logic.
• Use: Quick code edits, batch comment generation.

Claude 3.7 Sonnet / Thinking and Claude Sonnet 4 / Thinking

• Pros: Balanced between Haiku and Opus. Good context size.
• Cons: Slightly older reasoning stack.
• Use: General code documentation, bug reproduction analysis.

Claude 3.5 Sonnet / Claude 4 Opus (BYOK)

• BYOK indicates “Bring Your Own Key,” unavailable under your Pro plan.

SWE-1 / SWE-1.5

• Pros: Fine-tuned specifically for software engineering (Windsurf-native). Excellent at file grouping, dependency awareness, and commit structure.
• Cons: Limited creative language ability; narrow code-domain coverage outside mainstream languages.
• Use: End-to-end PR generation, repo diff analysis, structured refactoring.

Gemini 2.5 Pro

• Pros: Strong reasoning in web and Python environments, integrated tool reasoning.
• Cons: Context truncation at long inputs.
• Use: Web or API-driven code integration, especially for mixed content (e.g., Python + JSON pipelines).

xAI Grok Code Fast

• Pros: Fast responses, good JavaScript/TypeScript support.
• Cons: Shorter context, inconsistent language reasoning.
• Use: Front-end or lightweight script generation.

Kimi K2

• Pros: Efficient multilingual code understanding.
• Cons: Limited context size, weaker API understanding.
• Use: Small logic units or translation between languages.

Qwen3-Coder / Fast

• Pros: Excellent for Python, Java, and C++. Efficient low-cost coder.
• Cons: Moderate reasoning; struggles on high-level design.
• Use: Quick code writing, automated test generation.

o3 / o3 (high reasoning)

• Pros: Logical rigor, concise output.
• Cons: Slightly mechanical phrasing, lower fluency in non-code tasks.
• Use: Formal logic, algorithm derivation, math-driven code.

DeepSeek-V3-0324 / R1

• Pros: Compact, efficient, low credit; handles tabular data well.
• Cons: Weak abstraction capability.
• Use: Data cleaning, simple code automation.

| | | |

| --- | --- | --- | | | | | | | | | | | | | | | | | | | | | | | | |

Would you like me to rank them specifically for Windsurf’s Cascade coding agent workflows (e.g., file refactoring, code commenting, dependency tracing)? That would refine the practical guidance further.

User: Yes.

Yes. I want a quick reference for when I'm using Windsurf. I need to easily and quickly determine which model to select, depending on the current task at hand.

Assistant: Condensed Windsurf model reference:

Condensed Windsurf model reference:

| | | |

| --- | --- | --- | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |

1. Large or multi-file logic? → GPT-5 High Reasoning

1. Medium scope or frequent edits? → GPT-5 Medium Reasoning
2. Pure commit or repo structure? → SWE-1.5
3. Need natural-language clarity? → Claude Sonnet 4.5
4. Speed matters more than depth? → Haiku 4.5 / Grok Code Fast
5. Algorithmic or math logic? → o3 High Reasoning
6. Tests or scaffolding? → Qwen3-Coder Fast

Keep GPT-5 Medium Reasoning as your default. Switch up only when task scope, cost, or latency changes the priority.

User: I noticed you didn't include the models "Kimi K2" and "code-supernova-1-million".

I noticed you didn't include the models "Kimi K2" and "code-supernova-1-million". What are those best for?

Assistant: Here are quick summaries of Kimi K2 and code‑supernova‑1‑million, plus when they would make sense or not for your Winds…

Here are quick summaries of Kimi K2 and code‑supernova‑1‑million, plus when they would make sense (or not) for your Windsurf Cascade coding-agent workflows.

What it is:

• A Mixture-of-Experts (MoE) model by Moonshot AI. 1 trillion total parameters, about 32 billion active per input. Hugging Face
• Context window up to ~256 K tokens in its “Instruct” version. Together.ai Docs
• Optimised for “agentic” workflows: tool-use, reasoning, coding across large tasks. Weights & Biases

Strengths:

• Very large context window: helps when you dump many files, large codebases, or multi-file refactoring.
• Strong coding + reasoning capabilities: suited for complex architecture tasks or tooling chains.
• Good for agent-driven integration (e.g., multi-step workflows) rather than simple snippet generation.

Weaknesses / trade-offs:

• Likely higher latency and cost compared to smaller models.
• Possibly overkill for small tasks (single file, trivial edits).
• May require careful prompt engineering to maximise the MoE benefits.

When to use in Windsurf Cascade:

• Use when you are orchestrating or refactoring a large project: e.g., “analyse entire repo, identify dependencies, propose module restructure”.
• Use when there is deep tool chaining: e.g., you want to auto-generate tests, docs, commit changes across many files, with reasoning about each step.
• Avoid if your task is short / isolated (better use a faster, cheaper model).

What it is:

• A newer coding-focused model variant with a very large context window (~1 million tokens) according to public write-ups. Cline
• Built for agentic coding (multimodal support like diagrams/screenshots, coding workflows) via platforms such as Kilo Code, etc. Cline
• There are notes this model may no longer be widely available (“stealth” release, shut-down in some platforms). blog.kilocode.ai

Strengths:

• Massive context window: great when you need to feed in large amounts of code, history, docs, PRs and have the model reason across them.
• Tailored for coding workflows: good for many files, complex tasks, possibly image/diagram input.
• Could be a “go-to” when context size bottleneck is the issue.

Weaknesses / trade-offs:

• Availability may be limited (might not be fully supported in your environment).
• Possibly less mature or less tested in production (given the “stealth” status).
• Might still be slower or more expensive than more standard models.
• If your task doesn’t require massive context, the large window is wasted overhead.

When to use in Windsurf Cascade:

• Use when you want to feed in a very large set of code/documentation: e.g., full codebase + history + design docs + tests + many modules.
• Use when you want to integrate diagrams/screenshots + code + natural-language reasoning (if supported).
• Avoid if your task is small or you don’t need the huge context window — use a lighter/cheaper model instead.

If I were to slot them into your “quick reference” list:

• Kimi K2: For large, complex, tool-heavy agent workflows with deep reasoning across many files.
• code-supernova-1-million: When context size is the limiting factor (massive codebase + docs + multimodal inputs) and you can accept potential cost/availability trade-offs.

If you like, I can update your quick reference table to include these two models with flags for cost/latency and task-size thresholds.

User: Yes, do that.

Yes, do that.

Assistant: Updated quick-reference table for Windsurf Cascade model selection:

Updated quick-reference table for Windsurf Cascade model selection:

| | | | | | |

| --- | --- | --- | --- | --- | --- | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |

1. Default → GPT-5 Medium Reasoning

1. Large logic trees → GPT-5 High Reasoning
2. Repo or commit work → SWE-1.5
3. Docs / clarity → Claude Sonnet 4.5
4. Speed first → Haiku 4.5 or Grok Code Fast
5. Math / logic → o3 High Reasoning
6. Huge context (>250 K) → Kimi K2
7. Extreme context (~1 M) → code-supernova-1-million

This table fits on one screen and covers all models accessible in your Windsurf Pro plan (excluding BYOK).

User: I've been experiencing errors in a particular part of a program.

I've been experiencing errors in a particular part of a program. I've spent significant time (more than 6 hours) trying to fix the problems with GPT-5 (high reasoning), but still the problems remain.

Which model do you recommend using instead of GPT-5 (high reasoning), or should I keep this model and continue with it's efforts?

Assistant: If GPT-5 High Reasoning has looped for hours without progress, stop using it for this bug.

If GPT-5 High Reasoning has looped for hours without progress, stop using it for this bug. The model is optimized for planning and large-context reasoning, not for breaking causal deadlocks in already-attempted code paths.

Switch model based on the type of failure:

| Symptom | Recommended model | Reason | | ---------------------------------------------------------------------- | ------------------------ | -------------------------------------------------------------------------------------- | | Complex but localized runtime error (stack trace, nulls, DB, or logic) | Claude Opus 4.1 Thinking | Excels at tracing execution flow and identifying where assumptions fail. | | Multi-file interaction or config mismatch | Kimi K2 | Very large context window lets it see the entire subsystem and correlate dependencies. | | Bug linked to commit history, structure, or code hygiene | SWE-1.5 | Designed for Windsurf repos; good at diff analysis and fix commits. | | Network, HTTP, or API-integration errors | Gemini 2.5 Pro | Strong reasoning on request/response and schema mismatches. | | Subtle logical or state-invariant bug | o3 High Reasoning | Stepwise formal logic avoids repetitive speculation. | | Need to generate failing tests to isolate cause | Qwen3-Coder Fast | Produces concise reproducible test cases quickly. |

Recommended next step: Use Claude Opus 4.1 Thinking to re-analyze the failure with this directive:

Goal: Identify exact root cause and fix for [describe failure].

Constraints:
* Produce a new hypothesis tree—no repetition of prior GPT-5 attempts.
* At each branch, state what evidence would confirm or falsify it.
* Stop after finding first falsifiable root cause; then generate a single verified patch and test.

If that model converges, let SWE-1.5 apply and validate the patch within your repo.

User: If I switch models mid-conversation, must I re-describe everything in detail, or will the new model be able to see the …

If I switch models mid-conversation, must I re-describe everything in detail, or will the new model be able to see the previous questions and answers from the original model?

Assistant: In Windsurf, when you switch models mid-conversation, the new model cannot see or inherit the prior model’s reasoning o…

In Windsurf, when you switch models mid-conversation, the new model cannot see or inherit the prior model’s reasoning or memory. It only sees the chat context that remains visible in the session window.

So:

• If you keep the same chat thread, the new model can read the text that’s already in that thread (your prior messages and the model’s responses).
• If you start a new chat or switch the active model from a blank context, it won’t have any access to what was said or done before.

Best practice: before switching, summarize the state of the problem and paste the key logs, code, or error traces again so the new model has all critical inputs without relying on hidden context.

User: To confirm and clarify, the new model will be able to see and read whatever conversation and text is within the current…

To confirm and clarify, the new model will be able to see and read whatever conversation and text is within the current chat window, so I would only need to explain/re-describe things that are not visible.

Assistant: Correct.

Correct. Any model you switch to in Windsurf can read everything visible in the current chat window. You only need to restate information that is not shown in that window—such as code, logs, or context from other threads or files.

gpt-5-thinking

User: I've been trying to debug an error that's preventing the persistence of settings in a web app's Admin Console.

I've been trying to debug an error that's preventing the persistence of settings in a web app's Admin Console. I've began debugging with model GPT-5 medium reasoning, but after multiple failed attempts to fix the problem -- or even identify the cause of the problem -- I then switched to GPT-5 high reasoning model.

Unfortunately, even GPT-5 high reasoning is unable to solve this problem. Both models are merely repeating themselves and performing essentially the same tasks over and over with no results. I've wasted nearly 3 hours on this problem, to no avail.

Which of the other available models should I change to? Which model is most suited to debugging, error identification & correction of this type, etc.?

Assistant: Switch to Claude Opus 4.1 Thinking for root-cause debugging.

Switch to Claude Opus 4.1 Thinking for root-cause debugging. It’s the best in your list at error localization and explaining failure chains across client → API → DB.

If that stalls, use this fallback order by scenario:

• Repo-wide or multi-file symptoms: Kimi K2. Very large context. Good for dumping logs, configs, and many files at once.
• Commit/diff-aware inspection or structured refactors: SWE-1.5. Strong on repo semantics and grouping related changes.
• Protocol/API or request/response anomalies (cookies, CSRF, caching, 4xx/5xx): Gemini 2.5 Pro. Good at HTTP traces and JSON/state reasoning.
• Logic/constraints bugs (validation, race conditions, invariants): o3 (high reasoning). Precise stepwise reasoning.
• Test scaffolding to reproduce the bug fast: Qwen3-Coder Fast. Generates focused unit/integration tests quickly.
• Pure code-search/boilerplate checks: GPT-5-Codex. Fast at locating misused APIs and writing minimal patches.

Minimal anti-looping prompt to give the chosen model:

Goal: Settings in Admin Console do not persist.

Context: [paste failing request log lines, server error excerpts, relevant files: handler, DTO/serializer, DB layer, cache, CSRF/config].

Constraints:
* Produce a 6-step investigation plan first.
* For each step, name the observable you need (log line, request/response diff, DB write result).
* After each hypothesis, show falsifying evidence or confirm with an artifact.
* No repeating prior steps. Each attempt must add a new hypothesis or new evidence.
* Output must include: (a) minimal repro, (b) failing request example, (c) patch or config diff, (d) verification command.

High-leverage checks for “settings not persisting” you can paste as tasks:

1. Client → API: Capture one save attempt with full request/response. Confirm 200/204 vs hidden 401/403/409/422. Check CSRF token, Content-Type, SameSite, CORS, and ETag/precondition headers.
2. Server handler: Verify route method matches client. Confirm body parsing and schema validation; log rejected fields.
3. Write path: Ensure transaction commits. Check ORM autocommit, await on async saves, and error swallowing.
4. Caching layer: Confirm cache write-through/invalidations. Look for stale reads after write.
5. Permissions: Verify feature flags/tenant filters. A missing tenant scope often reads the old row.
6. Serialization: Make sure returned payload echoes new values; mismatch implies write succeeded but read path differs.

Pick model by the bottleneck:

• Need deep causal trace → Claude Opus 4.1 Thinking.
• Need huge context of files/logs → Kimi K2.
• Need repo-structured fixes/commits → SWE-1.5.
• Need HTTP/state scrutiny → Gemini 2.5 Pro.
• Need invariant math-like reasoning → o3 high reasoning.
• Need fast tests → Qwen3-Coder Fast.

User: Which model is best for beginning development to add a quite complicated feature set into an already complicated repo?

Which model is best for beginning development to add a quite complicated feature set into an already complicated repo? To clarify, I need to add OpenAI's ChatKit and Responses API + Tools (including TTS/STT) to an existing project.

I've attached the .md file describing the plan. You must examine the plan to the extent necessary to determine which Windsurf model is best for this plan. Specifically, you should tell me which model will be best to start with (for creating the scaffolding/foundation and preparing everything), as well as which models should assist with each step of the plan.

Assistant: Start with GPT-5 High Reasoning for scaffolding.

Start with GPT-5 High Reasoning for scaffolding. It will plan the end-to-end glue across ChatKit, the Responses API, tools, retrieval, and voice I/O. Your plan couples all of these, so a long-context planner is optimal. HOSTED_ASSISTANT_VOICE_PLAN

Model map by plan phase

| Phase / Step | Primary model | Why | Assist models | | ------------------------------------------------------------------------------------------------------------------------ | ------------------------ | -------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------ | | Phase 1 — Chat foundation: integrate ChatKit UI → /chat calling Responses API, register tools, pass tenant metadata.

HOSTED_ASSISTANT_VOICE_PLAN | GPT-5 High Reasoning | Lays the scaffold, enforces request/response shapes, and threads metadata. | SWE-1.5 for structured repo diffs and clean commits; Claude Sonnet 4.5 for clear config and SDK docs text; Kimi K2 if you need to load many existing files at once. | | Ingestion pipeline: uploads → normalize/OCR → chunk → embed → vector_store.create; map tenant→store.

HOSTED_ASSISTANT_VOICE_PLAN | GPT-5 Medium Reasoning | Balanced throughput for boilerplate services and workers. | Qwen3-Coder Fast to generate batch jobs, schemas, and retries quickly. | | Phase 2 — Tool layer: sql_read, doc_summarize, compare_docs, with tenant validation and logging.

HOSTED_ASSISTANT_VOICE_PLAN | o3 High Reasoning | Precise contract/spec authoring and invariant checks on params and auth. | Gemini 2.5 Pro to wire HTTP/JSON, error mapping, and idempotency; SWE-1.5 to group function files and tests into coherent commits. | | Architecture flow glue: ChatKit ↔ Responses API with tools and vector store.

HOSTED_ASSISTANT_VOICE_PLAN | Claude Opus 4.1 Thinking | Strong at cross-component causality and failure paths. | GPT-5 High as alternate if latency is acceptable. | | Phase 3 — STT: Realtime API WebSocket streaming; capture mic frames and forward text to /chat.

HOSTED_ASSISTANT_VOICE_PLAN | Gemini 2.5 Pro | Good at evented streaming handlers and WebSocket state. | Grok Code Fast to draft front-end audio workers and frame queues. | | Phase 3 — TTS: audio/speech with gpt-4o-mini-tts or tts-1, return signed URL/base64; cache by (message_id, voice).

HOSTED_ASSISTANT_VOICE_PLAN | Claude Sonnet 4.5 | Clear server/controller code and response caching logic. | GPT-5 Medium to finalize SDK calls and response types. | | Phase 4 — UI voice controls: Hold-to-Speak, Speak Response, settings toggle.

HOSTED_ASSISTANT_VOICE_PLAN | Grok Code Fast | Fast JS/TS components and event wiring. | Haiku 4.5 for small, rapid UI edits. | | Phase 5 — Observability & security: per-tenant isolation, signed URLs, cost tracking.

HOSTED_ASSISTANT_VOICE_PLAN | SWE-1.5 | Commit hygiene, logging hooks, and policy checks across files. | o3 High to write assertions and guardrails; Qwen3-Coder Fast to generate probes and tests. | | Deliverables sweep: /chat, /ingest, /tts, /stt, ChatKit UI, tenant-aware File Search, logging/caching/costs.

HOSTED_ASSISTANT_VOICE_PLAN | Kimi K2 | Load many files and tickets at once for the final integration pass. | SWE-1.5 to batch and label commits coherently. |

Default starting point

• Start: GPT-5 High Reasoning until /chat and tool registration compile and pass basic e2e. HOSTED_ASSISTANT_VOICE_PLAN
• Then: switch to GPT-5 Medium for throughput on services and adapters.
• Use Kimi K2 only when you must reason over many files and logs simultaneously.

Anti-looping build prompt

Give the active model this control block to prevent repetition:

Project: Hosted assistant with ChatKit + Responses API + Tools + File Search + Voice I/O.

Constraints:
* Output a phased scaffold plan with file tree, interfaces, and minimal runnable stubs.
* Before writing code, list risks by phase and a test to falsify each risk.
* After each commit set, produce: (a) commands to run locally, (b) one curl example per endpoint, (c) a verification log expected pattern.
* Do not repeat previous attempts. Each iteration must add a new artifact or a refactor diff.

Notes tied to the plan

• Core stack: ChatKit UI, Responses API orchestration, custom tool endpoints, vector stores, Audio/Realtime APIs. HOSTED_ASSISTANT_VOICE_PLAN
• Voice flow and chosen audio models are fixed in the plan; keep server code aligned with those endpoints and params. HOSTED_ASSISTANT_VOICE_PLAN HOSTED_ASSISTANT_VOICE_PLAN

If you want, I can produce a one-screen “who to use when” card scoped only to your repo and this plan.