Editing Openai/691a14bb-ac48-8008-85d0-2e4fb3f780d1 (section)

== 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.

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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:

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===== 1. Large or multi-file logic? → GPT-5 High Reasoning =====
# Medium scope or frequent edits? → GPT-5 Medium Reasoning
# Pure commit or repo structure? → SWE-1.5
# Need natural-language clarity? → Claude Sonnet 4.5
# Speed matters more than depth? → Haiku 4.5 / Grok Code Fast
# Algorithmic or math logic? → o3 High Reasoning
# 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<ref>{{cite web|title=Hugging Face|url=https://huggingface.co/moonshotai/Kimi-K2-Instruct|publisher=Hugging Face|access-date=2025-11-17}}</ref>
* Context window up to ~256 K tokens in its “Instruct” version. Together.ai Docs<ref>{{cite web|title=Together.ai Docs|url=https://docs.together.ai/docs/kimi-k2-quickstart|publisher=Together.ai Docs|access-date=2025-11-17}}</ref>
* Optimised for “agentic” workflows: tool-use, reasoning, coding across large tasks. Weights & Biases<ref>{{cite web|title=Weights & Biases|url=https://wandb.ai/site/inference/moonshotai-kimi-k2/|publisher=Weights & Biases|access-date=2025-11-17}}</ref>

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<ref>{{cite web|title=Cline|url=https://cline.ghost.io/code-supernova-1-million/|publisher=cline.ghost.io|access-date=2025-11-17}}</ref>
* Built for agentic coding (multimodal support like diagrams/screenshots, coding workflows) via platforms such as Kilo Code, etc. Cline<ref>{{cite web|title=Cline|url=https://cline.bot/blog/code-supernova-stealth-model|publisher=cline.bot|access-date=2025-11-17}}</ref>
* There are notes this model may no longer be widely available (“stealth” release, shut-down in some platforms). blog.kilocode.ai<ref>{{cite web|title=blog.kilocode.ai|url=https://blog.kilocode.ai/p/code-supernova-is-shutting-down-heres|publisher=blog.kilocode.ai|access-date=2025-11-17}}</ref>

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:

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===== 1. Default → GPT-5 Medium Reasoning =====
# Large logic trees → GPT-5 High Reasoning
# Repo or commit work → SWE-1.5
# Docs / clarity → Claude Sonnet 4.5
# Speed first → Haiku 4.5 or Grok Code Fast
# Math / logic → o3 High Reasoning
# Huge context (>250 K) → Kimi K2
# 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:

<syntaxhighlight>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.

</syntaxhighlight>

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.