GPT-5.5 for coding
GPT-5.5 is the #2 pick on LLMDex's llm for coding ranking out of 8 models we track for this use case. Below, the specific reasons it slots where it does, and when you should reach for an alternative.
Updated
At a glance
- Rank
- #2 of 8
- Context
- 400K tokens
- Output / 1M
- Pricing not published
- Released
- Mar 2026
Why GPT-5.5 fits this task
Three things about GPT-5.5 that map directly onto what this task rewards: Industry-leading tool-use and function-calling reliability; Strong end-to-end agent performance across SWE-bench and GAIA; Polished multimodal grounding on screenshots and charts. Beyond the task-specific fit, GPT-5.5 also brings wide ecosystem support, chatgpt, realtime api, responses api, both of which compound when the workload broadens.
The criteria this task rewards
LLMDex ranks best llm for coding on 5 criteria , these are the axes the ranking uses, in priority order:
- SWE-bench Verified score on real repository tasks
- HumanEval / LiveCodeBench function-level accuracy
- Long-context handling for multi-file edits (≥128k tokens)
- Tool-use and function-calling reliability for editor agents
- Output cost per million tokens, coding agents burn output fast
How GPT-5.5 scores on each axis
Where GPT-5.5 costs you: pricing premium vs. open-weight alternatives. For most teams this is acceptable on this workload, the value of the strengths above outweighs the cost. For cost-bound workloads or teams with strict latency budgets, run an eval against the next two ranked models on real data before committing.
Strengths that pay off here
- Industry-leading tool-use and function-calling reliability
- Strong end-to-end agent performance across SWE-bench and GAIA
- Wide ecosystem support, ChatGPT, Realtime API, Responses API
- Polished multimodal grounding on screenshots and charts
Tracked weaknesses
- Pricing premium vs. open-weight alternatives
- Output cost climbs fast on agent loops with many reasoning tokens
- Stricter content policy than some peers for creative work
When to pick something else
If you can pay slightly more or accept slightly different tradeoffs, Claude Opus 4.7 from Anthropic ranks one position higher and tends to win on the hardest cases. Anthropic's mid-2026 flagship, ahead on SWE-bench, agent reliability, and writing quality.
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Other models for coding
- Claude Opus 4.7 for coding
Anthropic's mid-2026 flagship, ahead on SWE-bench, agent reliability, and writing quality.
Read guide - Claude Sonnet 4.6 for coding
Anthropic's mid-tier 4.6 release, the workhorse model behind most production Anthropic deployments.
Read guide - Gemini 3 Pro for coding
Google's late-2025 flagship, set new benchmarks on long-context, vision, and reasoning at competitive pricing.
Read guide - DeepSeek-V3 for coding
DeepSeek's flagship 671B-parameter MoE, frontier-level quality at a tiny fraction of frontier prices.
Read guide - GPT-5 for coding
OpenAI's unified flagship combining GPT-line breadth with built-in reasoning, replacing both GPT-4o and the o-series for most users.
Read guide
GPT-5.5 for other use cases
Direct comparisons
Frequently asked
Is GPT-5.5 good for coding?
GPT-5.5 is ranked #2 on LLMDex's coding list. OpenAI's mid-cycle GPT-5 refresh, improved reasoning, tool use, and multimodal grounding over the 2025 launch.How much does GPT-5.5 cost for coding?
OpenAI has not published per-token pricing for GPT-5.5 at the time of writing.What's a cheaper alternative to GPT-5.5 for coding?
The next ranked model on this task is Claude Sonnet 4.6. Compare both before committing.When should I NOT use GPT-5.5 for coding?
Tracked weakness: Pricing premium vs. open-weight alternatives. If that constraint is binding for your workload, the next-ranked model on this task is the safer pick.
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