GPT-6.1-SOL vs Gemini 4 Argon: Can They Pass?

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Domestic access: https://4sapi.org

Abstract

This document outlines a repeatable evaluation workflow for comparing gpt-6.1-sol and gemini-4-argon on TypeScript repository modification tasks. It focuses on verifiable, log-backed metrics and standardized cost calculation rules, rather than subjective quality ratings. At the time of writing, no validated, identical-environment test results are available for these two model identifiers. No performance ranking is issued here; the content serves as a test protocol, evidence checklist and accounting specification for future controlled benchmark runs. All experiments must preserve fixed code baselines, identical routing settings, complete request logs, token consumption records, CI artifacts and billing trails for audit.

1. Scope of Retrieval and Boundary of Evidence

The absence of discoverable public records does not equate to official non-existence of the models. This document only states that within the listed sources, search queries and cutoff timestamp, the author could not retrieve sufficient, independently verifiable supporting materials. The retrieval cutoff date is October 24, 2026.

The target sources include official documentation portals and third-party aggregation platforms: OpenAI pricing and model reference pages, Google Gemini developer documentation and pricing pages, OpenRouter catalog endpoints, and Together AI model directories. Core search patterns target exact identifiers gpt-6.1-sol and gemini-4-argon across official developer domains.

No verifiable third-party directory URLs, raw API JSON payloads, request IDs or timestamped snapshots were attached to the source materials used to draft this protocol. This document therefore does not treat these two model IDs as confirmed production-ready entries. If supplementary third-party directory evidence is added later, the submitted artifacts must contain these fields:

1. Raw, unedited directory page or API endpoint response body
2. Full HTTP status and response headers
3. UTC timestamps for capture
4. The model field returned by the upstream route
5. Request ID, account tier and geographic region
6. Pricing snapshot and unit rates
7. Cryptographic hash of the captured response content

Even with such artifacts, the safe framing remains: the gateway returned this model label at that capture time. Without formal model card publication from the upstream vendor, this cannot be extrapolated to confirm official ownership, context window limits, maximum output constraints or published API pricing.

2. Evaluation Metrics: Loggable, Numerate Outcomes

The benchmark only tracks five objective metrics. Subjective scores such as "code quality 8.5 / 10" are excluded, as they cannot be consistently recreated by independent auditors.

A usable patch does not guarantee correct code behavior. Validation must proceed through a fixed rubric: TypeScript compilation checks, unit and regression test runs, lint and static analysis, predefined hidden acceptance assertions, and human review where automated gates cannot fully capture intent drift. All human assessment must reference a pre-written rubric, and review logs must be retained. Review criteria cannot be modified after observing model outputs.

3. Cost Accounting Methodology

Cost calculation must include all consumed resources across retries, exploratory reads, repeated searches, failed test cycles and repair attempts. It is incorrect to only price the final successful request for a resolved task.

The base formula for a single request:

Request cost
= (input_tokens / 1_000_000) * input_unit_price
+ (output_tokens / 1_000_000) * output_unit_price

The effective cost for one successfully completed task:

Single successful task cost
= Sum of billed cost from every attempt and retry on that task
/ Number of fully successful tasks

Platforms often introduce differentiated pricing tiers. Separate line items must be tracked and computed when applicable: base input and output tokens, cached read and write token charges, reasoning token surcharges, tool-use billing, and gateway platform fees. A complete cost breakdown is structured as:

Total charge
= Base input token cost
+ Base output token cost
+ Cache read token cost
+ Cache write token cost
+ Reasoning token cost
+ Tool invocation cost
+ Gateway or platform surcharge

When using aggregated routing layers such as OpenRouter or 4sapi as a forwarding gateway, configuration records must explicitly capture route identifier, target model ID, and each distinct unit price. Parameters that remain unverified are marked null rather than hard-coded into calculation pipelines. A sample configuration record:

{
  "route": "openrouter-or-4sapi",
  "model": "gpt-6.1-sol",
  "currency": "USD",
  "cached_input_per_million": null,
  "cached_output_per_million": null,
  "input_per_million": null,
  "output_per_million": null,
  "platform_fee": null
}

At this stage, base unit pricing and real-world token consumption figures remain unvalidated for both candidate IDs, so no dollar-ordered cost ranking can be produced.

4. Decision Framework Before Valid Benchmark Data

Before controlled test runs complete, no definitive ranking is issued. Guidance for different stakeholder requirements is structured as follows:

Missing data is explicitly declared as missing. Engineering judgments are only made after the evidence set is complete.

5. Benchmark Run Design

The planned sample design uses 20 distinct TypeScript tasks, with each task repeated 5 times for each candidate model ID. This creates 100 total task runs per model. This is a minimum exploratory sample; small sample sizes make P95 tail statistics highly unstable and prone to noise.

Tasks cover realistic repository modification patterns, with standardized acceptance gates:

• Type refactoring: adjust type unions, interfaces and associated consumption sites
• Bug repair: resolve runtime errors and regression failures
• Feature addition: introduce new behaviors guarded by new test suites
• Module restructuring: split packages while preserving public API contracts
• Configuration and tooling updates: adjust linters, build scripts and CI workflow definitions

Every trial starts from an identical Git commit baseline. Residual files, cache state and conversation history from prior runs are purged before each invocation. The full environment manifest must be logged for reproducibility: Node.js version, TypeScript version, package lock state, operating system and CPU architecture, memory and CPU quota, network egress profile, gateway endpoint, account tier, agent prompt template, task temperature, maximum output token cap, context compression policy, retry and backoff rules.

Equal treatment is enforced across candidates. One model cannot be assigned extra retry budget, longer timeouts or modified prompt wording. Unequal run parameters produce a comparison of run conditions, not model performance.

6. Three Separate Success Definitions

To avoid conflating patch format validity, first-attempt correctness and final repair outcomes, three distinct success layers are tracked independently.

1. First-pass patch applicability

The model returns a complete, parseable patch block. The patch applies cleanly, with no merge conflicts, and no destructive side effects outside the target files. This gate only validates output formatting, not functional correctness.

1. First-run test success

After applying the initial patch, the pipeline runs dependency installation, TypeScript compilation, linting, static checks, unit tests and regression suites. All gates pass without human modification. This metric isolates how often the first generated solution meets acceptance rules without repair cycles.

1. Final task success

Within a predefined retry budget and time ceiling, the agent completes all required changes automatically, or with allowed human review gates, and satisfies all acceptance criteria. This is the metric used for effective task cost calculation. First-run success must still be published alongside final success, to prevent heavy retry loops from masking poor initial quality.

7. Structured Tool Call Logging

Tool usage should not only be summarized with a raw count. Each invocation record preserves structured fields: tool type (read, search, edit, test, shell), start and end UTC timestamps, input and output byte or token volume, pass/fail/timeout status, repeat status (identical parameters to a prior call), and associated request ID. This breakdown exposes redundant exploration, failed reads and search loops that inflate latency and cost.

For long conversational agent sessions, additional tracking can capture instruction drift. A task may start with one modification target, then receive clarifying user instructions. The log must preserve this sequence, to distinguish model capability limitations from ambiguous task framing.

8. Gateway Routing Contamination Risk

Routing layers are a major confounding variable. Queueing delay, rate limits, cache behavior, automatic fallback logic and geographic load balancing all modify P95 latency and effective billing, independent of the underlying model.

The gold-standard experimental design is:

1. Run both candidate models through the identical gateway route
2. Use identical model labels, account tier and geographic region
3. Lock temperature, token caps, retry and timeout rules
4. Capture request IDs for every outbound call
5. Record both client-side and server-side timestamps
6. Isolate runs so one candidate’s traffic profile cannot shape the gateway state for the other

When shared routing is impossible, the route becomes an independent experimental variable. Results are grouped under the tuple: candidate model ID × gateway route × region × client version. Results cannot be aggregated across heterogeneous routes and attributed purely to model differences. A trial run on gateway A for one model and gateway B for the other cannot produce a valid head-to-head comparison.

9. Minimum Required Artifacts for Publication

Any claimed benchmark result must be accompanied by the complete audit bundle. The artifact list includes:

• Fixed repository commit hash
• Full task specification and hidden acceptance rubric
• Raw prompt payloads and full response bodies
• Patch diffs generated on every attempt
• CI build, lint and test logs
• Token consumption breakdown separated by input, output and cache categories
• Billing line items mapped to individual request IDs
• Request timestamps, timeout and failure markers
• Human review notes and signed-off evaluation outcomes

If third-party gateway identity cannot be independently validated, conclusions are bounded strictly: this is the observed behavior of that route, that account tier, that region and that task set. No overgeneralization to the broader upstream vendor model family is permitted.

10. Conclusion

As of October 24, 2026, this document contains no completed paired benchmark runs for gpt-6.1-sol and gemini-4-argon inside TypeScript code repositories. It cannot declare a winner, nor state that one model is faster, cheaper or more reliable.

Before valid comparison is possible, two prerequisite phases must complete. First, researchers must confirm the identity, official specifications and published pricing for each candidate model ID. Second, benchmark runs execute under identical baselines, tool permissions, routing rules and statistical design, with the full audit trail retained. Once the artifact set is complete, the metrics defined here can support evidence-backed ranking. Until that point, this text functions only as a prescriptive benchmark protocol and cost accounting standard.