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Card · LLM for code · updated 2026-07-11

GPT-5.5

OpenAI · flagship agent · reasoning up to xhigh · 1.05M context · closed weights

67.0%

DeepSWE pass@1 · independent measurement

#14out of 27 models
Editorial review 2026-07-11

OpenAI's flagship model for agentic coding: handles long multistep tasks, takes them through diff and validation, and saves tokens vs. its predecessors. On DeepSWE in top xhigh mode - pass@1 67.0%: behind GPT-5.6 Sol (72.7%) and Fable 5 (69.9%). Since 09.07, it has had a successor at the same price - GPT-5.6.

$5/$30

api per 1M tokens · cached input $0.50

1.05M

api · 400k in codex

12.2025

model knowledge cutoff

updated November 7, 2026

live · cron snapshot 24h · pass@1 and task price - deepswe.datacurve.ai · lines - Effort levels of a single model

Contents

1.0Should you get it
2.0Access and payment
3.0Ranking position and benchmark variation
4.0Family, versions, and modes
5.0Pricing and channels
6.0Data transparency and where your code goes
7.0Comparison and questions

1.0

Should you get it

Use for

+primary brain for agentic coding: Codex, autonomous pipelines, long tasks
+complex PRs, architecture, debugging, and multi-file changes
+tasks where the cost of an error exceeds token costs
+top quality baseline: compare cheaper models against it

Do not use for

−mass simple edits - overpaying; gpt-5.4-mini or a cheaper tier is enough
−self-hosting and open-weight scenarios: weights are closed
−
−

Until 09.07 the ChatGPT subscription (Plus $20, Pro from $100) included GPT-5.5 in ChatGPT and Codex; since 09.07 OpenAI's main model is GPT-5.6. GPT-5.5 via API - $5/$30 per 1M tokens.

2.0

Access and payment

Codex

codex
/model gpt-5.5

In Codex, GPT-5.5 was the primary model until 09.07; it was replaced by GPT-5.6.

OpenAI API

curl https://api.openai.com/v1/responses \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{"model": "gpt-5.5", "input": "..."}'

Canonical id - gpt-5.5; reasoning effort is set by a parameter.

OpenRouter

curl https://openrouter.ai/api/v1/chat/completions \
  -H "Authorization: Bearer $OPENROUTER_KEY" \
  -d '{"model": "openai/gpt-5.5", "messages": [...]}'

Same price $5/$30; pay with crypto.

RU aggregator

curl https://api.proxyapi.ru/openai/v1/chat/completions \
  -H "Authorization: Bearer $PROXYAPI_KEY" \
  -d '{"model": "gpt-5.5", "messages": [...]}'

OpenAI-compatible format: only base_url and the key change.

One brain - different names · model ID by surface

OpenAI API	`gpt-5.5`	canonical
Codex	`gpt-5.5`	primary until 09.07; select via /model
OpenRouter	`openai/gpt-5.5`
Cursor	`gpt-5.5`	in model picker
RU aggregators	`gpt-5.5`	ProxyAPI / AITUNNEL / VseGPT

For the subscription - a ChatGPT account on Plus or higher; for the API - a platform.openai.com key with a balance.

3.0

Ranking position and benchmark variation

67.0%DeepSWE pass@1 · 07.2026independent measurement by deepswe.datacurve.ai; GPT-5.6 Sol max - 72.7%, Fable 5 - 69.9%

82.7%Terminal-Bench 2.0 · 04.2026vendor claim from the release

58.6%SWE-bench Pro · 04.2026here, Opus 4.8 leads with 69.2% - benchmarks diverge

Benchmarks differ - that is normal

Benchmarks have split, and that should be read honestly: Claude Opus 4.8 leads on SWE-bench Pro, GPT-5.5 on Terminal-Bench, and GPT-5.6 Sol has led DeepSWE since 10.07 (72.7%). The old SWE-bench Verified is contaminated and no longer distinguishes flagships - so we show the variation across independent measurements, not one convenient number.

model ≠ agent

Model score ≠ tool score: GPT-5.5 was tested until 09.07 both paired with Codex (agent benchmarks) and solo (model benchmarks). Compare identical configurations - DeepSWE fixes xhigh mode.

4.0

Family, versions, and modes

A model is a versioned series, not a point: OpenAI flagships update every 6–10 weeks. The card tracks the GPT-5.x family; stale versions move to deprecated.

GPT-5.6 (Sol/Terra/Luna)Jul 9, 2026flagshipnew three-tier generation: Sol - the same $5/$30, stronger on agent benchmarks

GPT-5.504.2026activeagentic coding, computer use; replaced by GPT-5.6 as flagship since 09.07

GPT-5.5-pro04.2026activetop $30/$180 per MTok tier - 6× more expensive, almost always excessive for code

GPT-5.4 / 5.4-mini02.2026activecheaper ($2.50/$15 and $0.75/$4.50): workhorse tier for simple tasks

GPT-5.2, GPT-5.3-codex2025deprecatedthere is no separate “-codex” line anymore - consolidated into gpt-5.5

Reasoning modes

Within one model - a reasoning effort ladder: the higher it is, the longer it thinks and the more it costs. Mode selection changes both quality and the bill more than choosing between adjacent versions.

01low / mediumfast answers and simple edits; save tokens, do not handle complex debugging

02highworkhorse mode for everyday agentic coding

03xhighmaximum quality on complex tasks - the DeepSWE measurement configuration (pass@1 67.0%). More expensive and slower: enable where the cost of an error is high

04gpt-5.5-proseparate pro tier $30/$180: research-class tasks, almost always excessive for coding

5.0

Pricing and channels

Access channels · typical coding month, $

$20

Plus

ChatGPT + Codex; limits in 5-hour windows

$100

Pro

limits ×5–20; heavy daily work

$33

API

~5 DeepSWE-class tasks per day ($6.61/task)

$55

RU aggregator

API price

$5 input / $0.50 cached / $30 output per 1M tokens

Above 272K input tokens in the prompt, the price rises: ×2 input and ×1.5 output for the entire session; data-residency-endpoints +10%.

OpenAI API docs · verified 2026-07-03

Where available

ChatGPT and Codex: Go, Plus, Pro, Business, Enterprise, Edu plans; API - metering

In Codex, it was the primary model until 09.07 (replaced by GPT-5.6); limits - messages per 5-hour window (Plus: 15-80 on gpt-5.5), with credit purchases beyond that.

Codex models · verified 2026-06-10

Modes

Reasoning effort from low to xhigh; DeepSWE measures xhigh

A separate top-tier gpt-5.5-pro costs $30/$180 per MTok - 6 times more expensive; before the 09.07 transition, gpt-5.5 xhigh was the primary choice for coding.

OpenRouter API · verified 2026-07-03

Prices go stale

Model price lists and aggregator exchange rates change more often than new versions are released. Each row above includes a verification date - do not trust figures without a date, including ours.

6.0

Data transparency and where your code goes

Vendor benchmarks

Terminal-Bench 2.0 - 82.7%; SWE-Bench Pro - 58.6%

Figures from the OpenAI release are vendor claims, not independent measurements; the independent snapshot is below.

OpenAI release · verified 2026-06-05

Independent snapshot

DeepSWE: pass@1 67.0%, pass@4 88.5%, $7.23/task

Independent measurement by deepswe.datacurve.ai (07.2026 snapshot); leader of previous snapshots, since 10.07 GPT-5.6 Sol is first (72.7%), Fable 5 second (69.9%). Artificial Analysis Coding Index - 76.4.

DeepSWE (Datacurve) · verified 2026-07-08

Data

API traffic is not used for training by default; in ChatGPT - depends on settings

Official OpenAI policy for API/Business: client data is not used for training without opt-in. Data is processed on servers outside the country.

OpenAI enterprise privacy · verified 2026-07-02

Weights

Closed weights - cannot be deployed locally

Open-weight alternatives for code - Kimi K2.6, GLM-5.x; they lag behind flagships in coding quality.

OpenAI API docs · verified 2026-07-03

Track record · recent events

2026-04-23release

GPT-5.5 release: agentic coding, computer use, knowledge work

Terminal-Bench 2.0 82.7%, SWE-Bench Pro 58.6% according to the vendor claim

2026-06-10policy

Lineup consolidation: gpt-5.2 and gpt-5.3-codex - deprecated

there is no separate flagship "-codex" branch anymore; gpt-5.5 - Codex default

2026-06-04release

DeepSWE snapshot: pass@1 70.0% - leader of the June measurement

independent benchmark deepswe.datacurve.ai; in the 10.07 snapshot GPT-5.6 Sol is first (72.7%)

If it does not work · top reasons

▸403 / unsupported_country - region block

▸429 / 529 - overload or rate limit

Check status.openai.com; reduce concurrency for batches. On a subscription - a 5-hour window limit: wait for reset or buy additional credits.

▸Context overflowed, auto-compact consumes details

Above 272K input, the price is ×2 - give the model an exact slice, not the whole repo. In Codex, context is 400K: split the task into stages.

▸model_not_found - invalid id

Check the id against the surface table: gpt-5.5 in API and Codex, openai/gpt-5.5 on OpenRouter. List of live models - GET /v1/models.

▸“The model got dumber” after a silent update

Check whether the snapshot and reasoning mode changed (low effort feels like degradation). Pin the version in the API request and compare on the same task.

7.0

GPT-5.5 or Claude Opus 4.8 - the flagship fork of spring 2026

Both are closed-class flagship agents. The fork is not “who is smarter,” but “which pipeline”: cheap mass grind in Codex versus depth on complex context in Claude Code.

GPT-5.5

67.0%

DeepSWE pass@1 · xhigh

Claude Opus 4.8

60.9%

DeepSWE pass@1 · max

Criterion	GPT-5.5	Claude Opus 4.8
Strength	agentic pipeline: long tasks, token savings	large codebase architecture, precise edits
API price (in/out per MTok)	$5 / $30	$5 / $25, but token usage is higher
Context	1.05M API / 400K Codex	1M
Native agent	Codex (default model)	Claude Code

→The 2026 norm - keep both subscriptions and route tasks by type: quality is near parity, but the genres differ.

Other paths

GPT-5.5 or GPT-5.6successor: Sol is cheaper ($4/$20 vs $5/$30), and since Sep 22 there is GPT-6 too

GPT-5.5 or Kimi K2.6open-weight candidate: self-host

Frequently asked questions

▸How much does GPT-5.5 cost via the API?

Officially $5 input / $30 output per 1M tokens (cached input $0.50). A typical task in Codex by our DeepSWE slice - $6.61.

▸

▸GPT-5.5 or Claude Opus 4.8 - which is better for code?

The benchmarks honestly disagree: Opus leads on SWE-bench Pro (69.2% vs. 58.6%), while GPT-5.5 leads on our anchor DeepSWE (70.0% vs. 58%). Successor GPT-5.6 Sol max - 72.7% DeepSWE vs. 70.0% for GPT-5.5 in the June snapshot. GPT-5.5 is 2 times cheaper than Opus on output and more token-efficient. Our verdict: GPT-5.6 for the current Codex pipeline, Opus for complex architecture in Claude Code.

▸What does xhigh mean and when is it needed?

It is the top reasoning effort: the model thinks longer and costs more, but is noticeably stronger on complex tasks. DeepSWE measures xhigh specifically. For minor edits, lower modes or a cheaper model are enough - there is no reason to pay the xhigh price for renaming a variable.

▸How much code fits in the context?

1.05M tokens via API - roughly 3-4 MB of source code, an entire average repository. But above 272K input, the price doubles, and Codex has a 400K context: in practice, it is more cost-effective to give the model a precise slice rather than the entire repo.

▸Subscription or API - which is more cost-effective?

If you work in Codex daily - a ChatGPT subscription (Plus $20, Pro from $100) is almost always cheaper than metering: limits are counted in messages, not tokens. The API is better for automations, CI, and when you need to control spending by task.

▸Can it be deployed locally?

No, the weights are closed. If you need self-hosting - look at the open-weight class: Kimi K2.6, GLM-5.x, DeepSeek V4. They trail GPT-5.5 on code, but they run on your own hardware.

▸Does the model train on my code?

For the API - no by default (official OpenAI policy for API and Business plans). In ChatGPT personal plans, training is managed through the Data Controls setting. Data is processed on servers outside the country.

GPT-5.5

OpenAI · flagship agent · reasoning up to xhigh · 1.05M context · closed weights

67.0%

DeepSWE pass@1 · independent measurement

#14out of 27 models
Editorial review 2026-07-11

$5/$30

api per 1M tokens · cached input $0.50

1.05M

api · 400k in codex

12.2025

model knowledge cutoff

updated November 7, 2026

live · cron snapshot 24h · pass@1 and task price - deepswe.datacurve.ai · lines - Effort levels of a single model

Contents

1.0Should you get it
2.0Access and payment
3.0Ranking position and benchmark variation
4.0Family, versions, and modes
5.0Pricing and channels
6.0Data transparency and where your code goes
7.0Comparison and questions

1.0

Should you get it

Use for

+primary brain for agentic coding: Codex, autonomous pipelines, long tasks
+complex PRs, architecture, debugging, and multi-file changes
+tasks where the cost of an error exceeds token costs
+top quality baseline: compare cheaper models against it

Do not use for

−mass simple edits - overpaying; gpt-5.4-mini or a cheaper tier is enough
−self-hosting and open-weight scenarios: weights are closed
−
−

Until 09.07 the ChatGPT subscription (Plus $20, Pro from $100) included GPT-5.5 in ChatGPT and Codex; since 09.07 OpenAI's main model is GPT-5.6. GPT-5.5 via API - $5/$30 per 1M tokens.

2.0

Access and payment

Codex

codex
/model gpt-5.5

In Codex, GPT-5.5 was the primary model until 09.07; it was replaced by GPT-5.6.

OpenAI API

curl https://api.openai.com/v1/responses \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{"model": "gpt-5.5", "input": "..."}'

Canonical id - gpt-5.5; reasoning effort is set by a parameter.

OpenRouter

curl https://openrouter.ai/api/v1/chat/completions \
  -H "Authorization: Bearer $OPENROUTER_KEY" \
  -d '{"model": "openai/gpt-5.5", "messages": [...]}'

Same price $5/$30; pay with crypto.

RU aggregator

curl https://api.proxyapi.ru/openai/v1/chat/completions \
  -H "Authorization: Bearer $PROXYAPI_KEY" \
  -d '{"model": "gpt-5.5", "messages": [...]}'

OpenAI-compatible format: only base_url and the key change.

One brain - different names · model ID by surface

OpenAI API	`gpt-5.5`	canonical
Codex	`gpt-5.5`	primary until 09.07; select via /model
OpenRouter	`openai/gpt-5.5`
Cursor	`gpt-5.5`	in model picker
RU aggregators	`gpt-5.5`	ProxyAPI / AITUNNEL / VseGPT

For the subscription - a ChatGPT account on Plus or higher; for the API - a platform.openai.com key with a balance.

3.0

Ranking position and benchmark variation

67.0%DeepSWE pass@1 · 07.2026independent measurement by deepswe.datacurve.ai; GPT-5.6 Sol max - 72.7%, Fable 5 - 69.9%

82.7%Terminal-Bench 2.0 · 04.2026vendor claim from the release

58.6%SWE-bench Pro · 04.2026here, Opus 4.8 leads with 69.2% - benchmarks diverge

Benchmarks differ - that is normal

model ≠ agent

Model score ≠ tool score: GPT-5.5 was tested until 09.07 both paired with Codex (agent benchmarks) and solo (model benchmarks). Compare identical configurations - DeepSWE fixes xhigh mode.

4.0

Family, versions, and modes

A model is a versioned series, not a point: OpenAI flagships update every 6–10 weeks. The card tracks the GPT-5.x family; stale versions move to deprecated.

GPT-5.6 (Sol/Terra/Luna)Jul 9, 2026flagshipnew three-tier generation: Sol - the same $5/$30, stronger on agent benchmarks

GPT-5.504.2026activeagentic coding, computer use; replaced by GPT-5.6 as flagship since 09.07

GPT-5.5-pro04.2026activetop $30/$180 per MTok tier - 6× more expensive, almost always excessive for code

GPT-5.4 / 5.4-mini02.2026activecheaper ($2.50/$15 and $0.75/$4.50): workhorse tier for simple tasks

GPT-5.2, GPT-5.3-codex2025deprecatedthere is no separate “-codex” line anymore - consolidated into gpt-5.5

Reasoning modes

01low / mediumfast answers and simple edits; save tokens, do not handle complex debugging

02highworkhorse mode for everyday agentic coding

03xhighmaximum quality on complex tasks - the DeepSWE measurement configuration (pass@1 67.0%). More expensive and slower: enable where the cost of an error is high

04gpt-5.5-proseparate pro tier $30/$180: research-class tasks, almost always excessive for coding

5.0

Pricing and channels

Access channels · typical coding month, $

$20

Plus

ChatGPT + Codex; limits in 5-hour windows

$100

Pro

limits ×5–20; heavy daily work

$33

API

~5 DeepSWE-class tasks per day ($6.61/task)

$55

RU aggregator

API price

$5 input / $0.50 cached / $30 output per 1M tokens

Above 272K input tokens in the prompt, the price rises: ×2 input and ×1.5 output for the entire session; data-residency-endpoints +10%.

OpenAI API docs · verified 2026-07-03

Where available

ChatGPT and Codex: Go, Plus, Pro, Business, Enterprise, Edu plans; API - metering

In Codex, it was the primary model until 09.07 (replaced by GPT-5.6); limits - messages per 5-hour window (Plus: 15-80 on gpt-5.5), with credit purchases beyond that.

Codex models · verified 2026-06-10

Modes

Reasoning effort from low to xhigh; DeepSWE measures xhigh

A separate top-tier gpt-5.5-pro costs $30/$180 per MTok - 6 times more expensive; before the 09.07 transition, gpt-5.5 xhigh was the primary choice for coding.

OpenRouter API · verified 2026-07-03

Prices go stale

Model price lists and aggregator exchange rates change more often than new versions are released. Each row above includes a verification date - do not trust figures without a date, including ours.

6.0

Data transparency and where your code goes

Vendor benchmarks

Terminal-Bench 2.0 - 82.7%; SWE-Bench Pro - 58.6%

Figures from the OpenAI release are vendor claims, not independent measurements; the independent snapshot is below.

OpenAI release · verified 2026-06-05

Independent snapshot

DeepSWE: pass@1 67.0%, pass@4 88.5%, $7.23/task

DeepSWE (Datacurve) · verified 2026-07-08

Data

API traffic is not used for training by default; in ChatGPT - depends on settings

Official OpenAI policy for API/Business: client data is not used for training without opt-in. Data is processed on servers outside the country.

OpenAI enterprise privacy · verified 2026-07-02

Weights

Closed weights - cannot be deployed locally

Open-weight alternatives for code - Kimi K2.6, GLM-5.x; they lag behind flagships in coding quality.

OpenAI API docs · verified 2026-07-03

Track record · recent events

2026-04-23release

GPT-5.5 release: agentic coding, computer use, knowledge work

Terminal-Bench 2.0 82.7%, SWE-Bench Pro 58.6% according to the vendor claim

2026-06-10policy

Lineup consolidation: gpt-5.2 and gpt-5.3-codex - deprecated

there is no separate flagship "-codex" branch anymore; gpt-5.5 - Codex default

2026-06-04release

DeepSWE snapshot: pass@1 70.0% - leader of the June measurement

independent benchmark deepswe.datacurve.ai; in the 10.07 snapshot GPT-5.6 Sol is first (72.7%)

If it does not work · top reasons

▸403 / unsupported_country - region block

▸429 / 529 - overload or rate limit

Check status.openai.com; reduce concurrency for batches. On a subscription - a 5-hour window limit: wait for reset or buy additional credits.

▸Context overflowed, auto-compact consumes details

Above 272K input, the price is ×2 - give the model an exact slice, not the whole repo. In Codex, context is 400K: split the task into stages.

▸model_not_found - invalid id

Check the id against the surface table: gpt-5.5 in API and Codex, openai/gpt-5.5 on OpenRouter. List of live models - GET /v1/models.

▸“The model got dumber” after a silent update

Check whether the snapshot and reasoning mode changed (low effort feels like degradation). Pin the version in the API request and compare on the same task.

7.0

GPT-5.5 or Claude Opus 4.8 - the flagship fork of spring 2026

Both are closed-class flagship agents. The fork is not “who is smarter,” but “which pipeline”: cheap mass grind in Codex versus depth on complex context in Claude Code.

GPT-5.5

67.0%

DeepSWE pass@1 · xhigh

Claude Opus 4.8

60.9%

DeepSWE pass@1 · max

Criterion	GPT-5.5	Claude Opus 4.8
Strength	agentic pipeline: long tasks, token savings	large codebase architecture, precise edits
API price (in/out per MTok)	$5 / $30	$5 / $25, but token usage is higher
Context	1.05M API / 400K Codex	1M
Native agent	Codex (default model)	Claude Code

→The 2026 norm - keep both subscriptions and route tasks by type: quality is near parity, but the genres differ.

Other paths

GPT-5.5 or GPT-5.6successor: Sol is cheaper ($4/$20 vs $5/$30), and since Sep 22 there is GPT-6 too

GPT-5.5 or Kimi K2.6open-weight candidate: self-host

Frequently asked questions

▸How much does GPT-5.5 cost via the API?

Officially $5 input / $30 output per 1M tokens (cached input $0.50). A typical task in Codex by our DeepSWE slice - $6.61.

▸

▸GPT-5.5 or Claude Opus 4.8 - which is better for code?

▸What does xhigh mean and when is it needed?

▸How much code fits in the context?

▸Subscription or API - which is more cost-effective?

▸Can it be deployed locally?

No, the weights are closed. If you need self-hosting - look at the open-weight class: Kimi K2.6, GLM-5.x, DeepSeek V4. They trail GPT-5.5 on code, but they run on your own hardware.

▸Does the model train on my code?