That model picker in your OpenClaw config? It determines cost per completed job, how reliably your agent follows SOUL.md instructions, and whether a large PR diff fits in one pass or gets chunked into lossy fragments.
Three flagship models compete for the spot: Gemini 3.1 Pro, GPT-5.5, and Claude Opus 4.7. One model gets my default recommendation. The other two earn it for specific use cases.
TL;DR
- Best default: Gemini 3.1 Pro. Fits the workload shape of most OpenClaw deployments: large-context code review, lowest cost per job, free dev tier, native multimodal.
- Best for autonomous agents: GPT-5.5. Leads reported agentic benchmarks such as Terminal-Bench 2.0, if your context stays under 128K tokens per call.
- Best for strict code review: Claude Opus 4.7. Leads reported SWE-bench Pro results (64.3% in Anthropic's evaluation), strong instruction adherence, often exhibits self-checking behavior in practice.
When Should You Choose Gemini 3.1 Pro?
Choose Gemini if your OpenClaw workflow involves:
- Reviewing large PRs or monorepos
- Combining SOUL.md, MEMORY.md, and code context in a single call
- Working with CI/CD artifacts like screenshots or recordings
- Iterating heavily on prompts during development
If your workload fits this pattern, Gemini is usually the most practical choice.
What OpenClaw Actually Demands from an LLM
A single OpenClaw task isn't a chatbot turn. It's a system prompt, plus SOUL.md content, plus MEMORY.md accumulated state, plus tool call payloads, plus multiple back-and-forth exchanges. In practice, OpenClaw tasks involve significantly higher token usage than typical chatbot interactions, due to accumulated context from SOUL.md, MEMORY.md, tool calls, and multi-step exchanges.
OpenClaw is a context-heavy system. The model is not just generating code; it is reasoning over accumulated state (SOUL.md, MEMORY.md, diffs, and tool outputs). That shifts the bottleneck from raw task performance to context handling and cost per call.
Reliable tool-calling degrades first when context fills up. Instruction adherence drops next, especially with layered SOUL.md rules. Context window behavior under load, whether the model actually reasons over tokens near the middle of a long input, determines whether single-pass analysis works or just looks like it works.
Two Workload Categories
OpenClaw deployments generally fall into two patterns:
Large-context analysis covers code review, diff reasoning, and repo-wide changes. These jobs load full PR diffs alongside SOUL.md, MEMORY.md, and surrounding file context into a single call. Token counts are high, and the ability to reason across the entire input in one pass matters more than raw task-completion speed.
Multi-step autonomous tasks involve planning, tool use, and execution loops. The model runs multiple shorter calls, each with moderate context, to complete a sequence of actions. Benchmark scores on agentic task completion are the best proxy for performance here.
Many OpenClaw workflows lean toward large-context analysis, particularly for code review and CI/CD automation. That workload pattern, not just price or benchmarks, should drive model selection. Gemini 3.1 Pro supports large-context workflows without requiring chunking in many cases. GPT-5.5 is built for the second category. Claude Opus 4.7 brings the highest coding-specific benchmark scores and strict instruction adherence across both.
The Three Contenders at a Glance
Gemini 3.1 Pro: 1M token context, $2/$12 per 1M tokens (standard context) (≤200K tokens; $4/1M above that threshold), free dev tier through Google AI Studio, natively multimodal across text, images, audio, and video.
GPT-5.5: 128K context on the standard API, $5/$30 per 1M tokens, the highest Terminal-Bench 2.0 score of the three at 82.7%.
Claude Opus 4.7: 1M context at flat pricing, $5/$25 per 1M tokens, leads reported SWE-bench Pro results (64.3% in Anthropic's evaluation).
Quick Reference Table
| Dimension | Gemini 3.1 Pro | GPT-5.5 | Claude Opus 4.7 |
|---|---|---|---|
| Context window | 1M tokens | 128K (standard API) | 1M tokens |
| Input price ($/1M) | $2.00 (≤200K) / $4.00 (>200K) | $5.00 | $5.00 |
| Output price ($/1M) | $12.00 | $30.00 | $25.00 |
| Free dev tier | ✅ Google AI Studio | ❌ | ❌ |
| Terminal-Bench 2.0 | 68.5% | 82.7% | 69.4% |
| SWE-bench Pro | — | — | 64.3% |
| Multimodal (audio/video) | ✅ native | ⚠️ limited | ❌ |
Evaluation Criteria
Benchmark numbers are drawn from vendor announcements and should be interpreted accordingly. SWE-bench Pro scores come from Anthropic's Opus 4.7 announcement. Pricing is sourced from each provider's official API documentation.
Four dimensions, weighted for OpenClaw production use: context window capacity, cost per completed job, code review quality on benchmarks, and multimodal support for CI/CD workflows.
For OpenClaw workloads, context capacity and cost per job often matter more than raw benchmark scores.
Context Window
Gemini 3.1 Pro
Gemini 3.1 Pro offers a 1M token context window, one of the largest available at standard API pricing. For OpenClaw code review, that means single-pass ingestion of a full PR diff plus surrounding file context, SOUL.md, and MEMORY.md without splitting the input.
Single-pass analysis can capture cross-module relationships that chunked approaches miss. When a renamed interface in one file breaks three consumers in another, Gemini can process the full picture in one call. The tradeoff: requests exceeding 200K tokens are billed at $4.00/1M input instead of the standard $2.00.
Large-context models reduce the need for chunking, but can still struggle if prompts become too diffuse or contain conflicting instructions across files.
GPT-5.5
GPT-5.5's standard API context window is 128K tokens. Larger context tiers (such as 1M tokens) are not generally available on standard API access, which means it's not the default API experience most teams will use.
128K is not a soft limit you can occasionally brush against. A 200K-token PR diff plus SOUL.md plus task history fits in one Gemini call; it doesn't fit in GPT-5.5's standard tier at all. That forces chunked processing or context truncation, and for monorepo-scale OpenClaw workflows, chunking means the model never sees cross-file relationships in a single reasoning step.
Claude Opus 4.7
Claude Opus 4.7 provides a 1M token context window at flat pricing. A 900K-token request is billed at the same per-token rate as a 100K-token request, with no long-context surcharge.
For workloads that consistently exceed 200K tokens per call, Opus 4.7 has a pricing edge over Gemini's tiered model. If your typical OpenClaw job stays under 200K input tokens, Gemini's base rate ($2.00/1M) is cheaper by a wide margin.
Context Window Comparison
| Gemini 3.1 Pro | GPT-5.5 | Claude Opus 4.7 | |
|---|---|---|---|
| Max context | 1M | 128K (standard) | 1M |
| Long-context premium | ✅ above 200K | ⚠️ 1M not generally available on standard API | ❌ flat pricing |
| Monorepo-scale code review | ✅ | ❌ | ✅ |
Cost Per Completed OpenClaw Job
Gemini 3.1 Pro is typically the cheapest at standard on-demand pricing.
Gemini 3.1 Pro
At $2.00 input / $12.00 output per 1M tokens, an illustrative 10-call code review run with roughly 500K input tokens and 50K output tokens comes to approximately $1.60.
The free dev tier through Google AI Studio is a genuine advantage during development. Iterating on SOUL.md prompts and agent configurations without paying per call removes friction that adds up fast when you're tuning agent behavior. Rate limits on the free tier will constrain sustained production use, but for development and testing, nothing else matches it.
Gemini also supports OpenAI-compatible interfaces via certain endpoints and adapters (including Google AI Studio), which means teams can switch from another provider without rewriting integration code. The migration cost is a config change, not an engineering project.
GPT-5.5
GPT-5.5 is the most expensive at standard rates: $5.00 input / $30.00 output per 1M tokens. For the same illustrative scenario, the 10-call code review run costs approximately $4.00, or 2.5x what Gemini charges.
OpenAI claims GPT-5.5 uses fewer tokens to complete equivalent Codex tasks compared to prior models. Token efficiency partially offsets the higher per-token price on agentic runs. Batch and Flex pricing at half the standard rate is available for workloads that tolerate asynchronous processing.
Claude Opus 4.7
Opus 4.7's headline price is $5.00 input / $25.00 output per 1M tokens. For the same illustrative scenario, the 10-call run costs approximately $3.75 before accounting for tokenizer overhead.
Effective cost can increase depending on tokenization and prompt structure. Prompt caching (up to 90% savings) and batch processing (50% savings) can offset the increase, but only if your workload structure supports them. If you're not caching repeated SOUL.md content across calls, you're leaving money on the table.
Cost Comparison Table
These estimates assume approximately 500K input and 50K output tokens across 10 calls; actual costs vary by workload.
| Gemini 3.1 Pro | GPT-5.5 | Claude Opus 4.7 | |
|---|---|---|---|
| Input ($/1M) | $2.00 | $5.00 | $5.00 |
| Output ($/1M) | $12.00 | $30.00 | $25.00 |
| Free dev tier | ✅ | ❌ | ❌ |
| Est. 10-call run (illustrative) | ~$1.60 | ~$4.00 | ~$3.75+ |
| Hidden cost risk | ⚠️ extended context premium | ✅ token efficiency claim | ⚠️ possible tokenizer overhead |
Code Review Quality
Gemini 3.1 Pro
68.5% on Terminal-Bench 2.0 and 67.3% on GDPval, the lowest of the three on both agentic benchmarks. On BrowseComp (web research capability), it scores a competitive 85.9%.
Gemini's value in OpenClaw code review comes from fitting the workload pattern rather than leading on isolated coding tasks. Being able to hold an entire codebase diff in one pass means the model reasons over relationships between files that a higher-scoring model working on chunked input may not see together.
GPT-5.5
GPT-5.5 leads Terminal-Bench 2.0 at 82.7% and GDPval at 84.9%. BrowseComp at 90.1% makes it the strongest option when web research is part of the agent loop.
On workloads that fit within 128K tokens, GPT-5.5 will complete agentic tasks more reliably than either competitor.
Claude Opus 4.7
Opus 4.7 leads reported SWE-bench Pro results (64.3% in Anthropic's evaluation), the benchmark most directly tied to coding-specific tasks. Terminal-Bench 2.0 at 69.4% puts it slightly ahead of Gemini.
Two qualitative traits stand out for OpenClaw use: Opus 4.7 often exhibits self-checking behavior in practice (catching its own logical faults during planning) and strict instruction adherence. For teams with complex SOUL.md configurations, Opus 4.7's precision in following multi-layered instructions reduces false positives in code review output.
Benchmark Summary
| Benchmark | Gemini 3.1 Pro | GPT-5.5 | Claude Opus 4.7 |
|---|---|---|---|
| Terminal-Bench 2.0 | 68.5% | 82.7% | 69.4% |
| GDPval | 67.3% | 84.9% | 80.3% |
| SWE-bench Pro | — | — | 64.3% |
| BrowseComp | 85.9% | 90.1% | 79.3% |
Multimodal Support for CI/CD Workflows
Gemini 3.1 Pro
Gemini 3.1 Pro natively processes text, images, audio, and video in a single API call. CI/CD build screenshots, architecture diagrams, and video recordings of failing test runs can all be included alongside code context.
No separate vision or audio model required. For teams whose OpenClaw workflows involve visual artifacts, Gemini handles the full spectrum without workarounds.
GPT-5.5
GPT-5.5 supports text, images, and audio. Video support is limited. Computer use and web search tools are available. The multimodal coverage is broad but stops short of Gemini's native four-modality input.
Claude Opus 4.7
Opus 4.7 handles text and images only, with improved resolution compared to Opus 4.6. CI/CD screenshots work well. Audio and video inputs are not supported. If your CI/CD pipeline produces screen recordings or audio logs, Opus 4.7 can't process them.
Multimodal Comparison
| Gemini 3.1 Pro | GPT-5.5 | Claude Opus 4.7 | |
|---|---|---|---|
| Images | ✅ | ✅ | ✅ (improved res) |
| Audio | ✅ | ✅ | ❌ |
| Video | ✅ | ⚠️ limited | ❌ |
| CI/CD screenshots | ✅ native | ✅ | ✅ |
Who Each Model Serves Best
Gemini 3.1 Pro: Best Default for Most OpenClaw Teams
Most OpenClaw code review workflows load more than 128K tokens per call. Gemini is built for that. A full PR diff, SOUL.md, MEMORY.md, and surrounding file context fit in a single 1M-token call without splitting or coordination logic.
- 1M context at $2/1M input for requests under 200K tokens, making it the cheapest large-context option for typical OpenClaw code review workloads
- Free dev tier removes iteration cost when tuning SOUL.md and agent behavior, a workflow step every OpenClaw team repeats frequently
- Native four-modality input covers CI/CD screenshots, audio logs, and video artifacts in a single call. No separate vision or audio model required.
- Supports OpenAI-compatible interfaces via certain endpoints and adapters (including Google AI Studio), meaning switching from another provider is a config change, not a migration project
- Single-pass monorepo analysis keeps full cross-file context intact in one call
Coding benchmark scores (68.5% Terminal-Bench, 67.3% GDPval) are the lowest of the three on isolated task precision. If your typical job consistently exceeds 200K input tokens, the $4/1M rate erodes the pricing advantage fast. And the free tier rate limits will not sustain production workloads, requiring a paid plan for anything beyond development and testing.
GPT-5.5: Best for Autonomous Multi-Step Workflows
82.7% on Terminal-Bench 2.0 is the strongest agentic score of the three by a wide margin. GPT-5.5's standard context window, however, is 128K tokens. Teams whose primary metric is agentic task completion rate, on workloads that fit within that limit, get the most reliable execution from GPT-5.5.
Token-efficient completions partially offset the higher per-token price, according to OpenAI's claims about reduced token usage per task.
A large PR diff combined with SOUL.md, MEMORY.md, and multi-turn task history will exceed 128K tokens in many monorepo workflows, forcing you to chunk inputs and lose cross-file context. And at an illustrative cost of ~$4.00 per 10-call run, GPT-5.5 is 2.5x the cost of the same workload on Gemini 3.1 Pro.
Claude Opus 4.7: Best for Code Review Precision
64.3% on SWE-bench Pro in Anthropic's evaluation is the highest coding-specific score of the three. For teams where a false positive in code review means a wasted engineering cycle, that number matters more than Terminal-Bench.
Opus 4.7 often exhibits self-checking behavior in practice, catching logical faults during planning and reducing false positive rates in code review output. Strict instruction adherence makes it the strongest option for teams with complex, multi-layered SOUL.md configurations.
The cost picture is more nuanced than the headline $5/$25 suggests. Effective cost can increase depending on tokenization and prompt structure. Prompt caching and batch processing can offset this, but only if your workload supports them. And no audio or video support limits usefulness for CI/CD workflows involving non-text, non-image artifacts.
Frequently Asked Questions
Which model has the largest context window for OpenClaw?
Gemini 3.1 Pro and Claude Opus 4.7 both support 1M tokens at the standard API level. GPT-5.5's standard API is 128K; larger context tiers (such as 1M tokens) are not generally available on standard API access. For monorepo code review, Gemini or Claude are the practical choices.
Does Gemini 3.1 Pro work with OpenClaw out of the box?
Yes. Google AI Studio provides an OpenAI-compatible endpoint that works with OpenClaw's provider configuration. The full setup walkthrough is in the Gemini + OpenClaw guide. The free dev tier is available immediately; rate limits apply on sustained production workloads.
Why is GPT-5.5 the best on benchmarks but not the top recommendation?
The Terminal-Bench 2.0 lead (82.7%) is legitimate, but GPT-5.5's standard context window is 128K tokens. Large OpenClaw workflows regularly exceed that limit. Combined with 2.5x the cost of Gemini at standard API rates, the benchmark advantage doesn't offset the practical constraints for most teams. The typical OpenClaw workload is large-context code review, and GPT-5.5's standard tier doesn't fit that pattern.
Is Claude Opus 4.7 actually more expensive than the headline price suggests?
Effective cost can increase depending on tokenization and prompt structure. The $5/$25 headline price is unchanged, but the same input text may consume more tokens. Prompt caching (up to 90% savings) offsets the increase for repeated content like SOUL.md.
Can I switch models mid-project in OpenClaw?
Yes. OpenClaw supports any OpenAI-compatible endpoint, so the model is a config-level change. SOUL.md and MEMORY.md files are model-agnostic, though agent behavior may vary between models due to differences in instruction interpretation.
Which model handles CI/CD screenshot analysis best?
Gemini 3.1 Pro processes images, audio, and video natively. Claude Opus 4.7 supports images with improved resolution over its predecessor. GPT-5.5 supports images and audio; video support is limited.
Why does context size matter so much for OpenClaw?
Because OpenClaw tasks combine multiple sources of context (SOUL.md, MEMORY.md, code diffs, and tool outputs) into a single reasoning step. If that context exceeds the model's limit, it must be split across multiple calls, which adds complexity and can reduce reasoning quality across the full diff.
Where This Recommendation Breaks Down
Gemini 3.1 Pro is not a universal default.
If your OpenClaw workflow depends on tight iterative loops with smaller contexts, or if instruction-following precision is the primary constraint, GPT-5.5 or Claude Opus 4.7 may perform better despite higher cost or smaller context windows.
Final Verdict
Why Gemini 3.1 Pro Is the Default Choice
Gemini 3.1 Pro matches how most OpenClaw teams actually work: loading 200K+ token PR diffs alongside SOUL.md, MEMORY.md, and surrounding file context into a single call, then running multi-step reviews without splitting inputs.
- Large-context workflows without chunking. 1M tokens covers full PR diffs, SOUL.md, MEMORY.md, and surrounding file context in a single pass.
- No need to split inputs, coordinate multiple calls, or merge partial reasoning across chunks. Simpler agent architecture, fewer failure modes.
- Lowest cost per completed job. For an illustrative scenario, ~$1.60 per 10-call run vs ~$4.00 for GPT-5.5.
- Free iteration tier during SOUL.md development. Google AI Studio's free tier removes per-call cost from the prompt tuning cycle.
- Native multimodal for CI/CD pipelines. Images, audio, and video in one API call. No separate vision or audio model required.
- Config-level migration via OpenAI-compatible interfaces. No integration rewrite required.
Summary Table
| Dimension | Gemini 3.1 Pro | GPT-5.5 | Claude Opus 4.7 |
|---|---|---|---|
| Context window | ✅ 1M tokens | ⚠️ 128K standard | ✅ 1M tokens |
| Cost per job (illustrative) | ✅ lowest (~$1.60) | ❌ highest (~$4.00) | ⚠️ mid (~$3.75+) |
| Agentic task completion | ⚠️ 68.5% Terminal-Bench | ✅ 82.7% Terminal-Bench | ⚠️ 69.4% Terminal-Bench |
| Code review precision | ⚠️ no SWE-bench data | ⚠️ no SWE-bench data | ✅ 64.3% SWE-bench Pro |
| Multimodal (audio/video) | ✅ native | ⚠️ limited | ❌ |
| Free dev tier | ✅ | ❌ | ❌ |
| Long-context flat pricing | ⚠️ premium above 200K | ❌ | ✅ |
Gemini 3.1 Pro Is the Right Default
Most OpenClaw teams are running code review on repositories that exceed 128K tokens. That single fact eliminates GPT-5.5's standard tier from consideration for the majority of production workloads. Between the two 1M-context options, Gemini's cost advantage is substantial: for an illustrative scenario, roughly $1.60 per 10-call run compared to $3.75+ for Opus 4.7.
The free dev tier removes friction during the SOUL.md iteration cycle that every OpenClaw team goes through repeatedly. Native multimodal support covers CI/CD screenshots and video artifacts without extra configuration or separate model calls. And Gemini supports OpenAI-compatible interfaces via certain endpoints and adapters (including Google AI Studio), meaning zero migration effort if you're switching from another provider.
Gemini 3.1 Pro wins on the combination of workload fit, cost, multimodal coverage, and developer experience. At scale, the $2.40 per-job savings over Opus 4.7 (and $2.40 more over GPT-5.5) compounds into the difference between a sustainable deployment and one that gets cut in a budget review.
When to Choose GPT-5.5 Instead
Your workflow is primarily autonomous multi-step task completion rather than large-context code analysis. Context stays within 128K tokens per call. Your team is already invested in the OpenAI Agents SDK ecosystem and the switching cost outweighs the per-token savings.
When to Choose Claude Opus 4.7 Instead
Code review false positive rate is your primary production concern. Your SOUL.md configurations are complex enough that strict instruction adherence is a requirement, not a nice-to-have. You have prompt caching in place to offset Opus 4.7's higher effective cost.
