GPT-6.1 Sol vs Grok 4.7: Pricing, Benchmarks, and Model Routing

You should deploy GPT-6.1 Sol for immediate production workloads and hold off on Grok 4.7 until xAI publishes verified rate cards and academic benchmarks. At Layer3Labs, we build custom agents and workflow automations across production environments, and we evaluate frontier model releases against real operational budgets and access constraints. Choosing between GPT-6.1 Sol vs Grok 4.7 requires balancing OpenAI's published pricing against xAI's operational claims.

OpenAI announced GPT-6.1 Sol on September 29, 2026, during DevDay 2026. The release introduced the model under the Application Programming Interface (API) identifier gpt-6.1-sol, pitching near-flagship intelligence at one fifth the token price of GPT-6 Astra. The release arrived one week after the initial GPT-6 Sol launch on September 22, 2026.

xAI released Grok 4.7 on September 21, 2026, stating that the model runs twice as fast at half the operational cost of comparable frontier models. However, xAI has published no per-token rate card or standard benchmark suite to substantiate those claims. Engineering teams must differentiate between binding API commitments and marketing statements.

Evaluating both releases comes down to cost verification, live access channels, and deployment boundaries. A technical decision between these models cannot rely on speculation. Teams need concrete figures to plan architecture and protect enterprise budgets.

GPT-6.1 Sol vs. Grok 4.7: Side-by-Side

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GPT-6.1 Sol vs Grok 4.7 Token Pricing and Rate Cards

OpenAI publishes fixed token pricing for GPT-6.1 Sol, while xAI provides no public rate card for Grok 4.7. OpenAI charges $2.00 per million input tokens and $10.00 per million output tokens for standard API calls. Prompt cache reads on GPT-6.1 Sol cost $0.10 per million tokens, representing a 95 percent discount compared to standard input rates.

In contrast, xAI claims Grok 4.7 operates at half the cost of comparable frontier models without listing token figures. For context, Grok 4.6 lists standard rates of $2.00 per million input tokens and $6.00 per million output tokens. Applying xAI's half-the-cost claim implies roughly $1.00 / $3.00 per million tokens, a derived inference from xAI's half-the-cost claim, not a rate card.

Inference economics shift substantially when cached inputs dominate total token volume. OpenAI cut the cached input rate on GPT-6.1 Sol to half of the predecessor GPT-6 Sol rate. Teams verifying current rates should check the official OpenAI pricing page and monitor announcements on xAI news for official rate card updates.

• GPT-6.1 Sol standard input pricing is confirmed at $2.00 per million tokens.
• GPT-6.1 Sol standard output pricing is confirmed at $10.00 per million tokens.
• GPT-6.1 Sol cached input tokens cost $0.10 per million tokens, a 95 percent discount.
• Grok 4.7 pricing is roughly $1.00 / $3.00 per million tokens, a derived inference from xAI's half-the-cost claim, not a rate card.

→ Compare per-token pricing for every AI model

Immediate Access and Platform Availability

GPT-6.1 Sol is available immediately across commercial interfaces, whereas Grok 4.7 is reached through xAI's cloud API with enterprise terms for regulated use. OpenAI deployed GPT-6.1 Sol to developers through its standard API under the model identifier gpt-6.1-sol. Paid ChatGPT subscribers on Plus, Pro, Business, Enterprise, and Edu plans can access the model inside ChatGPT Work and Codex.

OpenAI confirmed that GPT-6.1 Sol is not available in standard ChatGPT Chat. Furthermore, OpenAI announced a GPT-6.1 Sol Ultrafast variant that delivers up to 8x faster token generation in Codex, scheduled for release in the coming days. Developers can test these tools directly within the OpenAI platform.

Access to Grok 4.7 requires navigating xAI's enterprise sales and infrastructure requirements. The model runs as a proprietary, cloud-hosted API with no on-premises deployment options. Regulated organizations must establish a dedicated enterprise API tier with a signed Business Associate Agreement (BAA) before processing protected customer records.

• GPT-6.1 Sol is live in the OpenAI API, ChatGPT Work, and Codex.
• Standard ChatGPT Chat does not include GPT-6.1 Sol access.
• OpenAI announced an Ultrafast Codex variant with up to 8x faster generation coming soon.
• Grok 4.7 requires a dedicated enterprise API tier with a signed BAA for regulated teams.

Published Benchmark Scores and Agentic Evaluations

OpenAI published vendor-reported benchmark scores on coding evaluations for GPT-6.1 Sol, while xAI published no academic benchmark table for Grok 4.7. On the DeepSWE v1.1 benchmark evaluating real software bug fixes, OpenAI reported that GPT-6.1 Sol scored 75.2 percent. That score edged out OpenAI's flagship GPT-6 Astra, which scored 74.8 percent on the same evaluation.

On desktop automation tests, OpenAI reported that GPT-6.1 Sol achieved results within 2.1 percentage points of Astra on OSWorld 2.0 offline evaluations. OpenAI emphasized that Sol delivered this accuracy at one-seventh the cost per completed task compared to Astra. These figures are vendor-reported and have not been independently reproduced by third parties.

For broader industry context, Google DeepMind reported a score of 77.9 percent on DeepSWE v1.1 for Gemini 4 Argon, as documented by NeuralTrust. Because xAI published no academic evaluations or benchmark tables for Grok 4.7, technical buyers have no verified baseline to evaluate its reasoning against GPT-6.1 Sol. Claims of frontier parity must remain unverified until standardized testing occurs.

• DeepSWE v1.1 bug-fixing score for GPT-6.1 Sol is 75.2 percent (OpenAI-reported).
• GPT-6 Astra scored 74.8 percent on DeepSWE v1.1 (OpenAI-reported).
• Gemini 4 Argon reached 77.9 percent on DeepSWE v1.1 (Google-reported).
• OSWorld 2.0 offline performance for GPT-6.1 Sol landed within 2.1 percentage points of Astra.
• Grok 4.7 benchmark scores remain unpublished across all academic test suites.

Context Window Limits and Output Ceilings

Neither OpenAI nor xAI published official context window figures for these specific model revisions at release. OpenAI did not list a context-window or maximum output token figure on the official GPT-6.1 Sol release page. The earlier GPT-6 Sol listed a 1,050,000-token window and a 128,000-token maximum output, but engineers cannot assume GPT-6.1 Sol shares those parameters.

Similarly, xAI has not published uniform context window limits or output rate limits for Grok 4.7. By comparison, Google DeepMind confirmed a 1,000,000-token maximum output generation for Gemini 4 Argon while leaving its input window unstated. When architects construct automated pipelines, undefined context ceilings introduce memory allocation risks.

Production teams processing multi-file codebases or extensive legal records require verified buffer parameters to avoid truncated responses. Until OpenAI and xAI release detailed technical specifications, developers must test practical context thresholds inside development environments. Enterprise buyers should verify technical limits directly through their respective account representatives.

• GPT-6.1 Sol context-window and max-output limits are not published on the release page.
• GPT-6 Sol predecessor listed a 1,050,000-token window and 128,000-token max output.
• Grok 4.7 context window and output limits are not published uniformly by xAI.
• Gemini 4 Argon documented a 1,000,000-token single-generation output ceiling.

Workload Routing in a GPT-6.1 Sol vs Grok 4.7 Pipeline

Production systems should route deterministic logic to GPT-6.1 Sol and delay Grok 4.7 routing until xAI publishes binding service limits. Workloads involving code maintenance, automated refactoring, and agentic desktop execution fit GPT-6.1 Sol because its token costs and coding scores are documented. Routing to Grok 4.7 makes sense only for experimental setups where raw execution speed outweighs auditability.

Dual-model routing patterns often fail when teams assign complex reasoning jobs to unverified endpoints to capture hypothetical cost reductions. In our client engagements across document automation and customer service workflows at Layer3Labs, we observed that pipeline failures occur when teams force complex reasoning tasks into lower-cost tiers to save budget. A single broken workflow that requires manual intervention costs far more than marginal token savings.

A disciplined architecture routes requests based on verified capabilities rather than marketing promises. Teams should direct multi-step coding tasks to GPT-6.1 Sol through Codex or the API, while using cached inputs to secure the $0.10 per million token rate. Grok 4.7 evaluations should remain restricted to isolated sandboxes until xAI issues an official rate card.

• Route to GPT-6.1 Sol: Repository refactoring, automated bug remediation, Codex development, and multi-step agent actions.
• Route to Grok 4.7: Experimental speed testing, non-critical conversational drafts, and internal sandbox evaluations.
• Pipeline Safeguard: Enforce strict schema validation and monitor token cache utilization to capture the 95 percent discount.

The Verdict

GPT-6.1 Sol wins for software engineering pipelines, agentic coding workflows, and any environment where token pricing must be audited and budgeted. Its confirmed $2.00 input and $10.00 output rates, paired with a $0.10 cached input price and a 75.2 percent DeepSWE v1.1 score, make it a viable near-flagship engine. Grok 4.7 may win for specialized workloads where raw inference velocity is the only priority, provided the organization operates inside a dedicated enterprise API tier with a signed BAA.

GPT-6.1 Sol is not for organizations needing standard consumer chatbot interfaces, because OpenAI excluded it from standard ChatGPT Chat. Grok 4.7 is not for organizations with strict compliance, fixed-budget constraints, or auditable governance requirements. Teams with those requirements cannot deploy models lacking public rate cards, published context limits, or standardized academic benchmarks.

Our recommendation would change if xAI publishes a binding per-token rate card matching its derived $1.00 / $3.00 estimate and releases third-party benchmark evaluations validating its frontier reasoning. Conversely, our recommendation would shift if OpenAI limits API throughput on GPT-6.1 Sol or prices the upcoming Ultrafast variant at a substantial premium. To implement an effective routing framework, audit your existing token consumption and deploy GPT-6.1 Sol vs Grok 4.7 based on verified operational constraints.