GPT-5.3 Codex XHigh
abacus/gpt-5-3-codex-xhigh出品方： abacus · 系列: gpt · 发布 2026-02-05 · 知识截止: 2025-08-31
⚠ 本模型为社区微调 / 衍生版本，非厂商官方发布。
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
能力清单
Model fit scores
0–100 · higher is betterThese scores reward declared capabilities, context size, price and provider availability — they are not benchmark results. Use them as a directional signal alongside your own evaluation.
Coding83
- Tool calling40/40
- Structured output20/20
- Reasoning10/10
- Context window (100K → 1M)12/20
- Provider availability1/10
Agents91
- Tool calling35/35
- Structured output25/25
- Reasoning15/15
- Output token limit15/15
- Provider availability1/10
JSON / structured output70
- Structured output / JSON mode50/50
- Tool calling20/20
- Temperature control0/10
- Price-friendly for high-volume0/20
Cost efficiency33
- Headline price (log-scaled)33/95
- Has prompt-cache pricing0/5
Long context70
- Context window (100K → 2M)60/90
- Has published price for full window10/10
Vision85
- Accepts image input50/50
- Context window (10K → 1M)24/30
- Has published price10/10
- Provider availability1/10
Production-readiness50
- Number of independent providers5/40
- Has published per-token price20/20
- Context window ≥ 8K15/15
- No data inconsistencies across providers10/10
- Official model (not derivative)0/15
Cost Efficiency Index
Open full calculator →Estimated cost using the recommended provider's headline rate. Each scenario fixes average input/output tokens — the assumptions are shown in the third column.
| Scenario | Cost | Assumption |
|---|---|---|
RAG answer per 1,000 RAG answers | $15.75 $0.02 per request | 5K input tokens (query + 4 retrieved chunks of ~1K each) and a 500-token answer. Typical SaaS knowledge-base bot. |
Support ticket triage per 10,000 tickets | $31.50 < $0.01 per request | 1K input tokens (ticket body + system prompt) and a 100-token JSON classification reply. High-volume customer support. |
Data extraction per 1,000 documents | $10.50 $0.01 per request | 2K input tokens (a single document page) and a 500-token JSON extraction. ETL / invoice / form pipelines. |
Code review per 1,000 PRs | $28.00 $0.03 per request | 8K input tokens (diff + surrounding files) and a 1K-token review comment. PR-bot workloads. |
Agent step per 1,000 steps | $29.40 $0.03 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
定价详情
推荐定价来自 abacus · gpt-5.3-codex-xhigh
在 1 家渠道可用
| 服务商 | 服务商模型 ID | 输入 / 1M | 输出 / 1M | 上下文 | 发布日期 |
|---|---|---|---|---|---|
| Abacus abacus | gpt-5.3-codex-xhigh | $1.75 | $14.00 | 400K | 2026-02-05 |
Frequently asked questions
How much does GPT-5.3 Codex XHigh cost?
GPT-5.3 Codex XHigh costs $1.75 per 1M input tokens and $14.00 per 1M output tokens, sourced from abacus. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.
What is the context window of GPT-5.3 Codex XHigh?
GPT-5.3 Codex XHigh has a context window of 400K tokens, with a max output of 128K tokens per reply. This is the total combined size of prompt + completion.
Does GPT-5.3 Codex XHigh support tool calling?
Yes. GPT-5.3 Codex XHigh supports tool calling (function calling). This makes it suitable for production agent and automation workloads where the model has to invoke external functions reliably.
Does GPT-5.3 Codex XHigh support structured output / JSON mode?
Yes. GPT-5.3 Codex XHigh supports structured output / JSON-schema-constrained decoding. This makes it suitable for production agent and automation workloads where the model has to invoke external functions reliably.
Can GPT-5.3 Codex XHigh accept image input?
Yes. GPT-5.3 Codex XHigh accepts both text and image input. Vision pricing per image is usually billed on top of the regular token rate — check abacus's docs for the exact rule.
Is GPT-5.3 Codex XHigh open-weight?
No. GPT-5.3 Codex XHigh is a proprietary model — only abacus (and any approved hosting partners) can serve it. The pricing above reflects the cheapest API access.
What are the best alternatives to GPT-5.3 Codex XHigh?
If GPT-5.3 Codex XHigh doesn't fit, consider o4-mini, Route LLM. Each one targets the same use case — see the Related links below for direct head-to-head pages.
Where does this data come from?
All numbers come from the public models.dev API and are normalised into a single canonical model record. We re-pull daily and write any changes (price, context, capability) to the /changelog page.
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最近更新：
Data is sourced from models.dev and normalized for comparison. Prices and capabilities may change. Always verify critical production decisions with the provider's official documentation.