Ox Alpha vs GPT-5.6 vs Claude 5: Which AI Coding Model Is Better?

Ox Alpha, GPT-5.6 Sol and Claude 5 can all power advanced coding agents, but they represent three very different technical and commercial propositions.

Ox Alpha is an anonymous preview model available through third-party AI gateways. It offers free access, video input and a one-million-token context window, but lacks a disclosed developer, permanent pricing, contractual stability and an official benchmark entry.

GPT-5.6 Sol is OpenAI’s flagship model for complex professional work. It combines strong coding performance with a mature tool ecosystem that includes hosted shell access, code execution, computer use, file search, web search, MCP and structured outputs.

Claude Opus 5 is Anthropic’s primary model for complex agentic coding and enterprise work. It currently holds the highest official DeepSWE result of the three, although its confidence interval overlaps with GPT-5.6 Sol.

The technical answer is therefore not that one model wins every category.

Quick verdict: Claude Opus 5 currently has the strongest verified DeepSWE result. GPT-5.6 Sol offers the most complete native tool environment for production agents. Ox Alpha is the most attractive free experimental option, but it has not demonstrated that it can outperform either model consistently.

Read our complete Ox Alpha AI guide for the model’s specifications, availability and unknown origin. Our separate analysis of the Ox Alpha benchmark results examines the original 80% claim, the larger 63% subset and the subsequent 62.8% complete community run.

Ox Alpha vs GPT-5.6 vs Claude 5 at a Glance

Feature	Ox Alpha	GPT-5.6 Sol	Claude Opus 5	Claude Fable 5
Developer	Undisclosed	OpenAI	Anthropic	Anthropic
Model ID	stealth/ox-alpha	gpt-5.6-sol	claude-opus-5	claude-fable-5
Context window	1,048,576 tokens	1,050,000 tokens	1 million tokens	1 million tokens
Maximum output	131,072 tokens	128,000 tokens	128,000 tokens	128,000 tokens
Text input	Yes	Yes	Yes	Yes
Image input	Yes	Yes	Yes	Yes
Video input	Yes	No	No	No
Tool calling	Yes	Yes	Yes	Yes
Structured output	JSON output without schema enforcement	Schema-supported structured outputs	Schema-supported structured outputs	Schema-supported structured outputs
Reasoning	Reasoning model	Configurable reasoning effort	Adaptive thinking	Always-on adaptive thinking
API availability	OpenRouter and OpenCode	Direct OpenAI API and supported platforms	Claude API and cloud platforms	Claude API and cloud platforms
Standard input price	Free during preview	$4 per million tokens	$5 per million tokens	$10 per million tokens
Standard output price	Free during preview	$20 per million tokens	$25 per million tokens	$50 per million tokens
Official DeepSWE result	None	73% ±3%	74% ±4%	70% ±4%
Community DeepSWE result	62.8% complete single run	Not applicable	Not applicable	Not applicable

Claude 5 is a model family rather than a single model. This comparison uses Claude Opus 5 as the primary competitor because it is positioned for complex agentic coding and currently leads the official DeepSWE leaderboard.

Claude Fable 5 is included where relevant because Anthropic describes it as its most capable widely released model for long-running agents. It is considerably more expensive and does not currently lead Opus 5 on DeepSWE.

Which Model Is Best for Coding?

Based on verified public evidence, Claude Opus 5 and GPT-5.6 Sol remain ahead of Ox Alpha.

The official DeepSWE results are:

Model configuration	DeepSWE result	Average output tokens	Average agent steps
Claude Opus 5, max reasoning	74% ±4%	118,000	99
GPT-5.6 Sol, max reasoning	73% ±3%	60,000	61
Claude Fable 5, max reasoning	70% ±4%	119,000	88
Ox Alpha, community full run	62.8%	Not officially reported	Not officially reported

The one-point difference between Claude Opus 5 and GPT-5.6 Sol is not statistically decisive. Their confidence intervals overlap significantly.

The operational difference may matter more than the raw percentage:

• Claude Opus 5 used approximately 118,000 output tokens and 99 agent steps per DeepSWE task.
• GPT-5.6 Sol used approximately 60,000 output tokens and 61 steps.
• GPT-5.6 Sol therefore reached a nearly identical completion rate with substantially fewer output tokens and agent actions.
• Ox Alpha’s complete community run reported 62.8%, but it was not produced as an official leaderboard configuration and has no published confidence interval.

Coding Verdict

• Best verified completion rate: Claude Opus 5
• Best verified efficiency among the leading results: GPT-5.6 Sol
• Best free coding experiment: Ox Alpha
• Best Anthropic model for maximum autonomous duration: Claude Fable 5
• Best model for production cannot be determined from DeepSWE alone: test the complete workflow

Ox Alpha Coding Performance

Ox Alpha’s reputation was initially driven by an 80% result on ten DeepSWE tasks.

That result was later followed by:

• approximately 63% on a larger subset;
• 62.8% on a complete community run;
• 26.6% from a single Terminal-Bench 3.0 pass;
• 37.2% micro recall on a 12-audit EVMBench security evaluation.

The complete DeepSWE community result places Ox Alpha around the performance range of DeepSeek V4 Pro rather than above Claude Opus 5 or GPT-5.6 Sol.

That is still a strong outcome for a free anonymous preview model.

Developer reports indicate that Ox Alpha performs well on:

• multi-file changes;
• repository-scale comprehension;
• long-running assignments;
• subagent coordination;
• complex instructions;
• backend implementation;
• code analysis;
• reasoning across large contexts.

Reported weaknesses include:

• slow completion at higher reasoning levels;
• occasional dead code;
• inconsistent frontend implementation;
• service availability errors;
• limited transparency about serving conditions;
• no guaranteed continuity after the preview.

Ox Alpha appears technically capable of serious software work. It should not yet be treated as a stable production dependency.

GPT-5.6 Sol Coding Capabilities

GPT-5.6 Sol is OpenAI’s flagship GPT-5.6 model for complex professional work, reasoning and coding.

Its technical specifications include:

• 1,050,000-token context window;
• 128,000 maximum output tokens;
• text and image input;
• configurable reasoning effort;
• function calling;
• schema-constrained structured outputs;
• streaming;
• prompt caching;
• direct access through Chat Completions and the Responses API.

Its supported reasoning levels are:

• none;
• low;
• medium;
• high;
• xhigh;
• max.

This allows developers to trade latency and token consumption against reasoning depth. Ox Alpha does not currently provide the same level of documented control because its operating details remain undisclosed.

GPT-5.6 Sol also supports a broad collection of managed tools through the OpenAI Responses API:

• web search;
• file search;
• image generation;
• code interpreter;
• hosted shell;
• patch application;
• computer use;
• MCP;
• skills;
• tool discovery.

The complete technical specifications are available on the official GPT-5.6 Sol model page.

Where GPT-5.6 Sol Is Strongest

GPT-5.6 Sol is the strongest option when coding is only one component of a larger workflow.

For example, an agent may need to:

1. inspect a code repository;
2. research current documentation;
3. modify several files;
4. run terminal commands;
5. interpret screenshots;
6. test the application;
7. interact with the interface;
8. generate documentation;
9. create supporting files;
10. deliver the completed result.

The model, tool infrastructure and API are controlled by the same identified provider. That reduces the number of external components required to build a production agent.

Claude Opus 5 Coding Capabilities

Claude Opus 5 is positioned by Anthropic for complex agentic coding and enterprise work.

Its technical specifications include:

• one-million-token context window;
• 128,000 maximum output tokens;
• text and image input;
• adaptive thinking;
• function calling;
• structured outputs;
• prompt caching;
• code execution;
• computer use;
• web search;
• direct access through the Claude Messages API.

Claude Opus 5 can also be deployed through:

• the Claude API;
• Amazon Bedrock;
• Google Cloud;
• Microsoft Foundry.

Anthropic describes Opus 5 as a model designed to maintain coherence across long, multi-step assignments, understand large codebases and handle complex feature development and debugging.

The current specifications for Opus 5, Fable 5 and Sonnet 5 are available in the official Claude model documentation.

Where Claude Opus 5 Is Strongest

Claude Opus 5 is particularly suitable for:

• complex repository analysis;
• difficult debugging;
• large refactoring assignments;
• feature implementation;
• code review;
• architecture planning;
• long-horizon tool use;
• assignments requiring sustained reasoning.

Its current DeepSWE result supports that positioning.

The trade-off is efficiency. Claude Opus 5 used almost twice as many output tokens as GPT-5.6 Sol during the official DeepSWE evaluation while producing a statistically similar completion rate.

For teams processing large volumes of coding work, that difference can materially affect cost and latency.

What Is Claude Fable 5?

Claude Fable 5 is Anthropic’s premium model for long-running autonomous agents.

It supports:

• one-million-token context;
• 128,000 output tokens;
• adaptive thinking that is always active;
• complex tool use;
• extended autonomous execution;
• large migrations;
• multi-day coding assignments.

Fable 5 costs $10 per million input tokens and $50 per million output tokens—twice the standard price of Claude Opus 5.

Its official DeepSWE result is 70% ±4%, below both Opus 5 and GPT-5.6 Sol.

This does not necessarily mean Fable 5 is a weaker model. DeepSWE measures a specific type of software-engineering performance. Fable 5 is designed for assignments whose duration and complexity may exceed the scope of an individual benchmark task.

For ordinary coding workflows, Opus 5 is currently the more rational Claude comparison. Fable 5 becomes relevant when the agent must remain autonomous for unusually long periods.

Context Window Comparison

All three model families provide approximately one million tokens of context.

Model	Context window	Maximum output
Ox Alpha	1,048,576	131,072
GPT-5.6 Sol	1,050,000	128,000
Claude Opus 5	1,000,000	128,000
Claude Fable 5	1,000,000	128,000

The numerical differences are operationally minor.

A million-token context window can potentially hold:

• a large source-code repository;
• extensive documentation;
• previous agent actions;
• logs and test results;
• technical specifications;
• issue histories;
• screenshots and other visual inputs.

Context capacity does not establish context quality.

A model must still:

• locate the relevant information;
• preserve instructions across a long session;
• avoid giving excessive weight to irrelevant files;
• manage tool results;
• maintain an accurate task state;
• recover when earlier assumptions are wrong.

The useful measurement is not how many tokens a model accepts. It is whether the agent completes the assignment reliably when the context becomes large.

Multimodal Input Comparison

Ox Alpha is the only model in this comparison explicitly listed as supporting video input.

Model	Text	Images	Video	Output
Ox Alpha	Yes	Yes	Yes	Text
GPT-5.6 Sol	Yes	Yes	No	Text
Claude Opus 5	Yes	Yes	No	Text
Claude Fable 5	Yes	Yes	No	Text

Video input could be useful for:

• examining recorded application failures;
• understanding a complete user journey;
• analysing interface behaviour;
• reviewing product demonstrations;
• identifying visual regressions across a sequence;
• converting recorded requirements into implementation tasks.

Its practical value depends on how accurately Ox Alpha processes temporal information. No comprehensive independent evaluation currently establishes its video-understanding performance.

For screenshot-driven interface work, all three providers offer visual input.

Tool Calling and Structured Output

Tool use is essential for coding agents because the model cannot complete software work by generating text alone.

An effective agent must be able to:

• read files;
• search repositories;
• edit code;
• execute commands;
• run tests;
• inspect errors;
• call external APIs;
• control browsers;
• update task state;
• return machine-readable results.

Ox Alpha

Ox Alpha supports:

• function calling through tools and tool_choice;
• JSON output through response_format;
• text, image and video input;
• OpenAI-compatible API requests.

Its structured JSON output does not currently provide JSON Schema enforcement through OpenRouter.

GPT-5.6 Sol

GPT-5.6 Sol supports:

• function calling;
• schema-constrained structured outputs;
• managed code execution;
• hosted shell;
• patch application;
• computer use;
• web and file search;
• MCP integrations;
• custom tools.

This is the most extensive native tool environment in the comparison.

Claude Opus 5

Claude Opus 5 supports:

• function calling;
• JSON Schema tool definitions;
• schema-constrained structured outputs;
• code execution in sandboxed containers;
• computer use;
• web search;
• custom client tools;
• programme-driven tool calling.

Claude’s tool environment is well suited to long-running agents, but implementation details differ depending on whether the model is accessed through the Claude API, a cloud provider or Claude Code.

How to Access Ox Alpha

Ox Alpha does not currently have a public first-party API operated under the identified name of its developer.

It can be accessed through two principal routes.

Ox Alpha Through OpenRouter

The official Ox Alpha listing on OpenRouter uses the following model ID:

stealth/ox-alpha

OpenRouter provides an OpenAI-compatible API, allowing the model to be connected to coding agents and applications that support configurable providers.

Current listed specifications include:

• free prompt tokens;
• free completion tokens;
• 1,048,576-token context;
• 131,072-token maximum output;
• function calling;
• JSON output;
• text, image and video input.

OpenRouter states that Ox Alpha’s anonymous provider retains prompts and completions but does not use them for model training.

Ox Alpha Through OpenCode

The OpenCode Zen documentation lists Ox Alpha Free with the following model ID:

x-preview-f-free

In an OpenCode configuration, the model is referenced as:

opencode/x-preview-f-free

The compatible endpoint is:

https://opencode.ai/zen/v1/chat/completions

OpenCode states that this route follows a zero-retention policy and does not use submitted data for model training.

The difference between the two routes is important:

Route	Model ID	Current price	Published retention position
OpenRouter	stealth/ox-alpha	Free	Provider retains prompts and completions; no training
OpenCode	x-preview-f-free	Free	Zero retention; no training

The model may be the same, but the gateway, contractual terms and published data-handling conditions are not identical.

API Pricing Comparison

The prices below represent standard direct API rates, not subscription plans or temporary third-party promotions.

Model	Input per million tokens	Cached input	Output per million tokens
Ox Alpha	Free during preview	Free	Free during preview
GPT-5.6 Sol	$4.00	$0.40	$20.00
Claude Opus 5	$5.00	$0.50	$25.00
Claude Fable 5	$10.00	$1.00	$50.00

GPT-5.6 Sol applies higher pricing when the prompt exceeds 272,000 input tokens. The complete request is then charged at:

• $8 per million input tokens;
• $0.80 per million cached input tokens;
• $30 per million output tokens.

Claude Opus 5 and Fable 5 do not use the same 272,000-token pricing threshold in their standard published rates.

Example Cost for a Coding Task

Consider an agentic coding assignment that consumes:

• 200,000 input tokens;
• 50,000 output tokens;
• no cached input;
• no separately priced tools.

The approximate model cost would be:

Model	Input cost	Output cost	Total
Ox Alpha	$0.00	$0.00	$0.00
GPT-5.6 Sol	$0.80	$1.00	$1.80
Claude Opus 5	$1.00	$1.25	$2.25
Claude Fable 5	$2.00	$2.50	$4.50

For a longer assignment using 500,000 input tokens and 50,000 output tokens:

Model	Input cost	Output cost	Total
Ox Alpha	$0.00	$0.00	$0.00
GPT-5.6 Sol	$4.00	$1.50	$5.50
Claude Opus 5	$2.50	$1.25	$3.75
Claude Fable 5	$5.00	$2.50	$7.50

In this long-context example, Claude Opus 5 becomes less expensive than GPT-5.6 Sol because OpenAI’s higher pricing applies to prompts exceeding 272,000 tokens.

These examples exclude:

• prompt caching;
• Batch API discounts;
• fast processing;
• tool-call charges;
• data-residency premiums;
• retries;
• failed agent runs;
• tokens consumed by additional verification.

Ox Alpha’s price advantage is temporary unless its anonymous provider announces permanent free access.

Data Retention and Privacy

The provider’s identity and route matter when an agent processes proprietary code.

Ox Alpha Through OpenRouter

OpenRouter states that the anonymous provider:

• retains prompts and completions;
• does not use them for training;
• operates under the Stealth Model Terms.

The provider’s legal identity and jurisdiction remain undisclosed.

Ox Alpha Through OpenCode

OpenCode states that the Ox Alpha provider:

• follows a zero-retention policy;
• does not use submitted data for training;
• hosts the model through OpenCode’s US-based infrastructure.

Businesses must evaluate the terms of the actual route being used, not assume that every Ox Alpha endpoint follows the same policy.

GPT-5.6 Sol

OpenAI states that API abuse-monitoring logs may contain prompts and responses and are retained for up to 30 days by default.

Qualified organisations can apply for modified abuse monitoring or Zero Data Retention, subject to endpoint and feature eligibility.

Claude Opus 5

Anthropic’s standard commercial and API retention period is 30 days. Zero Data Retention is available to qualified organisations for eligible models and features.

Claude Fable 5 is treated differently. It requires 30-day retention and is not available under a Zero Data Retention arrangement.

Privacy Verdict

• Best disclosed temporary policy: Ox Alpha through OpenCode
• Most problematic provenance: Ox Alpha through any route, because the model operator remains anonymous
• Best contractual clarity: GPT-5.6 Sol or Claude Opus 5 through their identified providers
• Not suitable for ZDR requirements: Claude Fable 5

A published zero-retention statement is useful. A named provider, enforceable agreement, audit process and stable jurisdiction are also important for production deployment.

Reliability and Production Risk

Ox Alpha’s largest disadvantage is not benchmark performance. It is operational uncertainty.

Businesses do not currently know:

• who owns the model;
• who trained it;
• whether the weights will remain available;
• whether the model ID will continue working;
• what permanent pricing will be;
• whether the free preview will be extended;
• whether the model will be renamed;
• what service-level agreement will be offered;
• whether future versions will preserve the same behaviour.

GPT-5.6 Sol and Claude Opus 5 have identified providers, published documentation, direct APIs, defined pricing, enterprise agreements and established support channels.

That makes them easier to adopt for production systems even when Ox Alpha is cheaper.

Model quality matters. Infrastructure continuity determines whether the system can remain operational.

Which Model Should You Choose?

Choose Ox Alpha When:

• you want to test an advanced coding model without token costs;
• the repository contains no confidential data;
• video input matters;
• you can tolerate preview instability;
• the model is not a permanent production dependency;
• you can switch providers if the endpoint disappears.

Choose GPT-5.6 Sol When:

• coding is part of a larger professional workflow;
• the agent needs shell access, patch application or computer use;
• you require a broad native tool ecosystem;
• execution efficiency matters;
• you need a disclosed provider and production API;
• you want configurable reasoning effort.

Choose Claude Opus 5 When:

• the assignment requires difficult repository-level reasoning;
• long-horizon coherence is more important than minimum cost;
• the agent performs complex debugging or refactoring;
• you want the strongest current official DeepSWE result;
• you need deployment through Anthropic or major cloud providers.

Choose Claude Fable 5 When:

• the assignment may continue autonomously for hours or days;
• maximum agent duration matters more than token cost;
• 30-day data retention is acceptable;
• the workflow justifies premium pricing.

Final Verdict

Claude Opus 5 currently has the highest verified DeepSWE result, but its advantage over GPT-5.6 Sol falls inside overlapping confidence intervals.

GPT-5.6 Sol achieved nearly the same completion rate using substantially fewer output tokens and agent steps. It also provides the broadest native tool infrastructure for agents that must move beyond code generation and complete full digital workflows.

Ox Alpha is technically impressive. Its 62.8% complete community DeepSWE run, Terminal-Bench result and EVMBench performance indicate that it belongs in serious coding-model evaluations.

It does not currently beat GPT-5.6 Sol or Claude Opus 5 on verified evidence.

The practical ranking is:

1. GPT-5.6 Sol — best overall production coding and tool-use system
2. Claude Opus 5 — best verified DeepSWE completion rate
3. Ox Alpha — best free experimental coding model
4. Claude Fable 5 — best premium option for unusually long autonomous assignments

The ranking changes if token price is the only criterion. Ox Alpha wins at zero cost.

It changes again if provider identity, contractual protection and long-term availability matter. Ox Alpha finishes last.

For a broader comparison across professional and business automation workloads, see our guide to the best AI models in 2026.

Frequently Asked Questions

Is Ox Alpha Better Than GPT-5.6?

No verified benchmark proves that Ox Alpha is better than GPT-5.6 Sol. Ox Alpha reported 62.8% in a complete community DeepSWE run, while GPT-5.6 Sol holds an official result of 73% ±3%.

Is Ox Alpha Better Than Claude 5?

There is no evidence that Ox Alpha consistently outperforms Claude 5. Claude Opus 5 currently leads the official DeepSWE leaderboard at 74% ±4%, while Claude Fable 5 records 70% ±4%.

Which Model Is Best for Coding?

GPT-5.6 Sol is the strongest overall production option because of its coding performance, execution efficiency and native tools. Claude Opus 5 has the highest official DeepSWE result. Ox Alpha is the best free option for controlled experiments.

Which Model Has the Largest Context Window?

The practical difference is negligible. Ox Alpha supports 1,048,576 tokens, GPT-5.6 Sol supports 1,050,000 tokens, and Claude Opus 5 supports one million tokens.

Which Model Supports Video Input?

Ox Alpha supports text, image and video input. GPT-5.6 Sol and Claude Opus 5 support text and image input but do not list native video input.

Is Ox Alpha Free?

Ox Alpha is currently free through OpenRouter and OpenCode during its preview period. Permanent pricing has not been announced.

Can Ox Alpha Be Used in OpenCode?

Yes. OpenCode lists Ox Alpha Free under the model ID x-preview-f-free. In an OpenCode configuration, the full reference is opencode/x-preview-f-free.

Can Ox Alpha Be Used Through OpenRouter?

Yes. The OpenRouter model ID is stealth/ox-alpha, and the model can be accessed through OpenRouter’s OpenAI-compatible API.

Which Model Is Cheapest for Long-Context Coding?

Ox Alpha is currently the cheapest because it is free during the preview. Among the identified commercial models in this comparison, Claude Opus 5 can become less expensive than GPT-5.6 Sol when an individual request exceeds 272,000 input tokens because GPT-5.6 applies higher long-context pricing.

Is Ox Alpha Safe for Proprietary Code?

Do not use Ox Alpha with proprietary code without reviewing the exact route and contractual terms. OpenRouter states that the anonymous provider retains prompts and completions. OpenCode publishes a zero-retention position for its Ox Alpha route, but the model operator remains anonymous.