📌 Quick answer:

• Everyday engineering: GPT-6.1 Sol or Claude Sonnet 5.5, both $1.60 / $8 per 1M tokens on Vidgo.
• Hard, multi-system investigations: Claude Opus 5.5, $3.20 / $16.
• High-volume, mechanical tasks: GPT-6 Luna ($0.08 / $0.40) or DeepSeek V4.1 Flash ($0.30 / $1.20).
• Code plus screenshots, docs or video: Gemini 3.8 Flash, $0.60 / $3.
• A second reviewer: Grok 4.7, $1.60 / $4.80.
• Try them side by side: every model here is available through Vidgo with one key; PoYo is an alternative route.

A good coding model solves the job in your repository, respects its constraints, and produces a change you can verify. This guide compares the models behind coding tools. If you use an IDE assistant or terminal agent, these are the models you would plug into it.

Model documentation and listed prices were checked on October 13, 2026. Prices are USD per one million tokens. Recommendations are based on published capabilities and pricing, not a Vidgo coding benchmark.

Best Coding LLMs at a Glance

Vidgo figures are the rates listed on the linked model pages; they are separate from official-provider prices. Cache reads, writes, long-context uplifts, tools, and retries can change the final bill.

How We Selected These Models

This guide focuses on current API models that can help with software engineering, rather than comparing complete IDE products or autocomplete subscriptions. A model cannot run a terminal simply because it can describe a shell command: an agent harness must provide tools, execute them, and return the results.

Use five criteria when choosing your shortlist:

1. Patch correctness: Does the change solve the stated problem and pass relevant tests?
2. Repository awareness: Does it follow local architecture, types, dependencies, and conventions?
3. Tool reliability: Does it call available tools correctly, recover from errors, and verify results?
4. Review effort: How much human work is needed to catch unsupported assumptions or unnecessary changes?
5. Cost per accepted change: Include retries, reasoning, cache operations, and unsuccessful attempts.

Public benchmark results can help identify candidates. They do not measure your codebase directly. Different tools, effort settings, prompts, task sets, and attempt counts can change a score; avoid comparing two percentages unless those conditions align. OpenAI likewise recommends evaluating representative workloads and recording success, latency, token usage, and cost. Source: OpenAI deployment checklist.

1. GPT-6.1 Sol: A Starting Point for Repository Work

Consider GPT-6.1 Sol when a task spans several files: tracing a failing request through a handler and service, planning a dependency update, or implementing a feature with validation and tests.

OpenAI positions it for complex coding and professional workflows. The official context window is 1,050,000 tokens, with a maximum 922,000-token input and 128,000-token output. Those limits provide room for source material; they do not make sending an entire repository the most efficient prompt. Supply the failing behavior, relevant files, constraints, and a way to check the result. Source: OpenAI GPT-6.1 Sol documentation.

Integration detail: use Responses for tool calling. Chat Completions supports the model without tool calling. Its reasoning settings start at low; none and minimal are unsupported. Confirm the tools your chosen gateway actually exposes before connecting an agent.

Choose it when: the work needs investigation and verification across multiple steps. Compare another tier when: a narrowly defined transformation accounts for most of your traffic.

2. Claude Sonnet 5.5: A Candidate for Daily Engineering

Sonnet 5.5 belongs on a shortlist for features with clear acceptance criteria, routine refactors, and bug fixes. At official standard rates of $2 input and $10 output, it costs half as much per token as Opus 5.5; Vidgo lists $1.60 and $8.

Its official specification includes a one-million-token context window and 128K maximum output. Migration still needs attention: forced tool use can return an error, and non-default temperature, top_p, or top_k values are rejected. The model also changes how text between tool calls appears. Source: Anthropic Sonnet 5.5 overview and migration notes.

A useful trial is a bounded change such as adding pagination to an existing API while preserving its response contract. Compare the resulting patch, tests, and reviewer corrections with GPT-6.1 Sol. That gives you evidence for your stack without assuming a universal winner.

3. Claude Opus 5.5: An Escalation Option for Difficult Work

Anthropic positions Opus 5.5 for extended coding-agent and knowledge-work tasks. That makes it a candidate for an intermittent race condition, a migration with conflicting requirements, or an investigation where several plausible causes must be ruled out. Its official standard rates are $4 input and $20 output; thinking is always enabled, with effort controlling its depth. Source: Anthropic Opus 5.5 documentation.

Use it when a successful investigation is worth the additional budget. Escalation is an evaluation strategy: if Sonnet already produces a correct, reviewable change, paying twice the token rate needs another benefit, such as fewer retries or less human investigation.

For a closer look at those two tiers, read Claude Sonnet vs Opus.

4. GPT-6 Sol and GPT-6 Luna: Keep the Workload Role Clear

GPT-6 Sol remains a comparison point for existing integrations. GPT-6.1 Sol and GPT-6 Sol share official standard short-context rates of $2 input and $10 output, while Luna is $0.10 and $0.50. Cached input differs: $0.10 for 6.1 Sol, $0.20 for Sol, and $0.01 for Luna. Source: OpenAI API pricing.

Luna is worth evaluating for constrained jobs such as classifying test failures, extracting symbols from supplied code, or converting repetitive fixtures to a known schema. Validate outputs automatically where possible and route failed cases to a stronger model. OpenAI identifies Luna with focused, repeatable work. Source: OpenAI model workload guidance.

Do not switch a functioning Sol agent solely by changing the model string. For native Chat Completions, Sol and Luna support function calling only with reasoning_effort: "none"; reasoning with tools belongs on Responses. Re-run your tool cycle and task evaluations after any switch.

5. Gemini 3.8 Flash: Coding with Multimodal Context

Gemini 3.8 Flash is a candidate when engineering evidence includes documents, screenshots, or other supported media alongside source code. Google describes it as generally available for long-horizon software engineering and agent workflows, with a one-million-token context window, 64K maximum output, and adjustable thinking levels. Source: Google Gemini 3.8 Flash guide.

The official standard introductory rates are $0.75 input and $3.75 output through December 31, 2026; published rates become $1.50 and $7.50 on January 1, 2027. Output billing includes thinking tokens. Source: Google Gemini API pricing.

For a useful trial, supply a UI screenshot, the relevant component, and a precise layout defect. Assess whether the patch fixes that defect without regressing keyboard behavior or responsive layout. Verify media and native-tool support on your selected gateway.

6. DeepSeek V4.1 Flash: A Cost-Sensitive Candidate

DeepSeek V4.1 Flash adds native visual understanding to a coding and reasoning workflow. Its official API uses deepseek-flash; older V4 Flash aliases temporarily route to the newer model. The release documentation also states that off-peak rates are half of peak rates. A gateway can use a different public model ID and its own billing schedule. Source: DeepSeek V4.1 Flash release notes.

Vidgo lists deepseek/deepseek-v4.1-flash at $0.30 input and $1.20 output. Evaluate it for code explanation, bounded fixes, and repeated coding-assistant requests where a low unit cost is useful.

Keep an eye on retry rate. A lower-priced model can be expensive if it repeatedly produces patches that need repair. Compare accepted changes and billed tokens rather than assuming identical task cost from a price table.

7. Grok 4.7: Consider the Native Tools You Need

Grok 4.7 is another candidate for coding, agent tasks, and code review. Its native guide lists Responses, function calling, web search, X search, and code execution, with a 500,000-token context and standard rates of $2 input and $6 output. Source: xAI Grok 4.7 developer guide.

Those native capabilities should not be copied into a gateway checklist automatically. Vidgo's current Grok 4.7 page lists Chat Completions and a 256K context. Confirm your required context length and tools through the route you plan to use.

Try it against the same defect and review criteria as your other shortlisted models. A lower output rate is useful only if the output meets your acceptance criteria.

Vertical Comparison: Which Model Tier Should Handle the Task?

A model router can save money when work is divisible. A practical policy is to try a focused model for mechanical tasks, use a general engineering model for implementation, and escalate a difficult investigation when needed. This policy is a starting hypothesis to test.

For a single request with 100K uncached input tokens and 10K billed output tokens, the listed Vidgo base rates give:

Calculation: input tokens / 1,000,000 × input rate + billed output tokens / 1,000,000 × output rate.

This holds token counts constant to illustrate pricing, not model efficiency. Include any chargeable reasoning in billed output, and add separate cache writes, tools, retries, or applicable long-context adjustments. Real agent runs make multiple requests and produce different token counts.

Which coding model should handle this task?

What kind of coding task is it?
 │
 ├── Mechanical: rename, extract, classify, convert fixtures ──► 【GPT-6 Luna or DeepSeek V4.1 Flash】
 ├── Scoped feature or bug fix with a test ────────────────────► 【Claude Sonnet 5.5 or GPT-6.1 Sol】
 ├── Multi-file agent work with tool calls ────────────────────► 【GPT-6.1 Sol (Responses)】
 ├── UI bug with a screenshot or a spec PDF ───────────────────► 【Gemini 3.8 Flash】
 ├── Intermittent bug across services, unclear cause ──────────► 【Claude Opus 5.5】
 └── Second opinion on a risky patch ──────────────────────────► 【Grok 4.7】

Horizontal Comparison: Coding Model Access Across Platforms

Compare the exact model, protocol, and tool combination before connecting a coding agent. Access to a model through a familiar API format does not establish support for all of its native features.

Kie.ai's model documentation also says some errors can arrive with HTTP 200, so inspect its documented response envelope when building an adapter.

For Vidgo and PoYo, start with the linked model pages and verify the request your agent actually sends, including tool definitions, streaming events, and usage. Listing two providers does not establish independent upstream capacity. Exercise a real failover case before relying on it.

Start a Coding Evaluation on Vidgo

Open the relevant model playground, paste a small failing function with its expected behavior, and compare the response with a known test. Then move to a repository task with a fixed acceptance check.

This text-only Responses request asks GPT-6.1 Sol to review a function:

curl --request POST https://api.vidgo.ai/v1/responses \
  --header "Authorization: Bearer $VIDGO_API_KEY" \
  --header "Content-Type: application/json" \
  --data '{
    "model": "openai/gpt-6.1-sol",
    "input": "Review this function. Identify edge cases, provide a minimal fix, and list tests: function average(values) { return values.reduce((sum, value) => sum + value, 0) / values.length; }",
    "reasoning": { "effort": "medium" },
    "max_output_tokens": 4096,
    "stream": false
  }'

It returns a model response; it does not execute tests or modify your repository. Keep the key on your server, inspect response usage, and follow the Vidgo Responses documentation when adding your own tool loop.

For a small but useful evaluation, use the same collection of recent bugs, feature tasks, and review examples across models. Hold tools, time budget, and acceptance checks constant. Record correct completion, reviewer changes, total spend, and end-to-end time. Repeat ambiguous results before changing your default.

Frequently Asked Questions

Which LLM should I try first for coding in 2026?

Start by comparing GPT-6.1 Sol with Claude Sonnet 5.5 on representative repository tasks. Add Opus 5.5 for difficult work and a focused lower-cost model for repetitive jobs. The right default is the model that meets your quality threshold with acceptable cost and review effort.

Is the cheapest model the best value?

Only when it completes your tasks reliably. Compare total cost per accepted change, including failed attempts and human review. A token-price advantage is one input to that decision.

Does an OpenAI-compatible API support every OpenAI tool?

No. Check the exact model, endpoint, and tool combination. For GPT-6.1 Sol, native tool calling requires Responses; Chat Completions access alone does not establish agent-tool compatibility.

Should I send the whole repository to a long-context model?

Start with the files and evidence needed for the task. Add more context when the investigation requires it. Large inputs affect cost and can exceed a gateway's limit even when the native model supports a longer window.

Can I keep several platforms behind one coding agent?

Yes, with a provider adapter that handles model IDs, authentication, streaming, tools, errors, and usage. A successful text request is not enough to validate an agent migration: test at least one complete tool-call-and-result cycle.

Choose Your Next Coding Model

Begin with GPT-6.1 Sol on Vidgo or Claude Sonnet 5.5 on Vidgo, and use Vidgo API documentation to connect your workflow. Compare the PoYo coding model catalog when evaluating another platform.

For related decisions, see Claude Sonnet vs Opus and the best Claude alternatives.

Sources

• OpenAI: GPT-6.1 Sol, API pricing, and deployment checklist.
• Anthropic: Sonnet 5.5 and Opus 5.5.
• Google: Gemini 3.8 Flash and API pricing.
• DeepSeek: V4.1 Flash release.
• xAI: Grok 4.7 developer guide.
• PoYo: GPT-6.1 Sol pricing and endpoints, and Kie.ai: GPT-6.1 Sol API.