GPT-5.3 Codex

OpenAIKey gpt-5.3-codexAlso reported as openai/gpt-5.3-codex, cx/gpt-5.3-codex, gh/gpt-5.3-codex, chatgpt/gpt-5.3-codex, freemodel-s/gpt-5.3-codex, github_copilot/gpt-5.3-codex

No usage of GPT-5.3 Codex in the last 7 days. Showing all time instead.

Rank
#6
by active users
Active users
1,334
All time
Adoption
54%
of 2,457 accounts
Tokens
1.7T
cache included
Est. cost
$594.7K
at public list prices
First seen
Feb 4, 2026
in any submission

Oct 10, 2024 – Nov 12, 2026 (UTC)2,457 accounts100% of tokens from an identified modelData through Nov 12, 2026, 07:50 UTCHow we count

Daily active users

Distinct accounts that used this model on each UTC day.

0 active users on Nov 12, 2026

Daily active users for GPT-5.3 Codex, Oct 10, 2024 to Nov 12, 2026. Peak 495. Use the left and right arrow keys to read each day.5002500
Show daily values
Daily active users and tokens for GPT-5.3 Codex
Date (UTC)Active usersTokens
Oct 10, 202400
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Jan 1, 202500
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Dec 25, 202500
Dec 26, 202500
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Dec 28, 202500
Dec 29, 202500
Dec 30, 202500
Dec 31, 202500
Jan 1, 202600
Jan 2, 202600
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Feb 4, 20261189.5M
Feb 5, 2026809.1B
Feb 6, 202628024.8B
Feb 7, 202624720.9B
Feb 8, 202626026.3B
Feb 9, 202632522.5B
Feb 10, 202634625.3B
Feb 11, 202636426B
Feb 12, 202636827.9B
Feb 13, 202641931B
Feb 14, 202633022.3B
Feb 15, 202633526.5B
Feb 16, 202636026.3B
Feb 17, 202636327.6B
Feb 18, 202638728.5B
Feb 19, 202643633.8B
Feb 20, 202643132.1B
Feb 21, 202635526.6B
Feb 22, 202636536.7B
Feb 23, 202644439.6B
Feb 24, 202648432.9B
Feb 25, 202648431.4B
Feb 26, 202646334.7B
Feb 27, 202646433.6B
Feb 28, 202637527.4B
Mar 1, 202636326.7B
Mar 2, 202643931.2B
Mar 3, 202649057.6B
Mar 4, 202648480.9B
Mar 5, 202649555.6B
Mar 6, 202641644.3B
Mar 7, 202625026.4B
Mar 8, 202623146.5B
Mar 9, 202627438.5B
Mar 10, 202623846.9B
Mar 11, 202623523.9B
Mar 12, 202621125.4B
Mar 13, 202619721.8B
Mar 14, 20261284.8B
Mar 15, 202614417.2B
Mar 16, 20261955.1B
Mar 17, 20261807.7B
Mar 18, 202617121.7B
Mar 19, 202616821.1B
Mar 20, 20261686.7B
Mar 21, 20261035.2B
Mar 22, 202610429.4B
Mar 23, 20261537.5B
Mar 24, 20261528.6B
Mar 25, 20261515.9B
Mar 26, 20261416.2B
Mar 27, 20261588.3B
Mar 28, 20261125B
Mar 29, 20261106B
Mar 30, 20261404.5B
Mar 31, 20261535.2B
Apr 1, 202614816.1B
Apr 2, 20261409B
Apr 3, 20261499B
Apr 4, 20261047.3B
Apr 5, 20261006.9B
Apr 6, 202610310.6B
Apr 7, 202612522.8B
Apr 8, 20261188.9B
Apr 9, 202612823.3B
Apr 10, 20261308.1B
Apr 11, 2026968.9B
Apr 12, 2026753.5B
Apr 13, 20261185.3B
Apr 14, 20261265.3B
Apr 15, 20261227B
Apr 16, 20261186.5B
Apr 17, 20261156.2B
Apr 18, 2026903B
Apr 19, 2026836.6B
Apr 20, 20261036.4B
Apr 21, 20261044.3B
Apr 22, 20261033.9B
Apr 23, 20261072.9B
Apr 24, 2026922.1B
Apr 25, 2026701.5B
Apr 26, 2026621.5B
Apr 27, 2026815.3B
Apr 28, 2026691.5B
Apr 29, 2026664.5B
Apr 30, 2026642.8B
May 1, 2026571.1B
May 2, 2026532.9B
May 3, 2026401B
May 4, 2026575.2B
May 5, 2026523.1B
May 6, 2026652.9B
May 7, 2026471B
May 8, 2026591.9B
May 9, 2026421.3B
May 10, 202635746M
May 11, 2026592.3B
May 12, 2026621.3B
May 13, 2026581.6B
May 14, 2026481.3B
May 15, 2026501.1B
May 16, 202632425M
May 17, 202627448.9M
May 18, 2026471.2B
May 19, 202652659.4M
May 20, 202648878.3M
May 21, 2026521.1B
May 22, 2026502.8B
May 23, 2026301.4B
May 24, 20264111.9B
May 25, 2026454.8B
May 26, 2026579.4B
May 27, 202644723.8M
May 28, 202640675.1M
May 29, 202638827.9M
May 30, 2026301.2B
May 31, 2026391.4B
Jun 1, 202662856M
Jun 2, 202627262.9M
Jun 3, 2026854.8M
Jun 4, 2026696.6M
Jun 5, 20269442.7M
Jun 6, 20265175.5M
Jun 7, 2026842.6M
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Jun 9, 202614107.5M
Jun 10, 20269125.6M
Jun 11, 2026974.2M
Jun 12, 20269136.7M
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Jun 14, 2026558.4M
Jun 15, 20267164.9M
Jun 16, 2026530.2M
Jun 17, 2026615.4M
Jun 18, 2026849.8M
Jun 19, 2026537.2M
Jun 20, 2026326.7M
Jun 21, 2026446.3M
Jun 22, 202610183.4M
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Jun 24, 202647.2M
Jun 25, 202611.3M
Jun 26, 20267104.3M
Jun 27, 2026684.1M
Jun 28, 2026460.3M
Jun 29, 2026637.7M
Jun 30, 2026784.5M
Jul 1, 20267118.4M
Jul 2, 20266121.1M
Jul 3, 20265125.3M
Jul 4, 2026532.2M
Jul 5, 2026329.6M
Jul 6, 2026839.6M
Jul 7, 2026552.5M
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Jul 9, 20265404.9M
Jul 10, 20265117.3M
Jul 11, 20263114.6M
Jul 12, 20261129.5M
Jul 13, 2026485.4M
Jul 14, 2026461.6M
Jul 15, 20265121.2M
Jul 16, 2026412.1M
Jul 17, 2026431M
Jul 18, 2026226.1M
Jul 19, 20262265.6M
Jul 20, 20262139.9M
Jul 21, 20266148.4M
Jul 22, 20264107.2M
Jul 23, 20262510.6M
Jul 24, 20264105.5M
Jul 25, 2026422.1M
Jul 26, 20262326.1M
Jul 27, 20263655.2M
Jul 28, 2026592.3M
Jul 29, 20265283.9M
Jul 30, 20263349M
Jul 31, 20263145.9M
Aug 1, 20264267.6M
Aug 2, 2026262.9M
Aug 3, 20261832K
Aug 4, 2026140K
Aug 5, 202645.3M
Aug 6, 20263127M
Aug 7, 202613.2M
Aug 8, 202611.5M
Aug 9, 202600
Aug 10, 2026136M
Aug 11, 20262185K
Aug 12, 2026123M
Aug 13, 202619.7M
Aug 14, 2026175.4M
Aug 15, 202619.7M
Aug 16, 20261109K
Aug 17, 202600
Aug 18, 20261298K
Aug 19, 202616.7M
Aug 20, 2026133M
Aug 21, 20263252.4M
Aug 22, 202612.5M
Aug 23, 20261312K
Aug 24, 202600
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Aug 27, 202615.4M
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Aug 31, 2026167.3K
Sep 1, 202600
Sep 2, 202611M
Sep 3, 2026225M
Sep 4, 2026331.6M
Sep 5, 20261151K
Sep 6, 202611.2M
Sep 7, 202600
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Sep 9, 2026375.6M
Sep 10, 2026296.2M
Sep 11, 2026216.2M
Sep 12, 2026128.5K
Sep 13, 202600
Sep 14, 2026563.8M
Sep 15, 2026615.8M
Sep 16, 2026264.2M
Sep 17, 2026212.2M
Sep 18, 2026158.8K
Sep 19, 202629.6M
Sep 20, 202614.4M
Sep 21, 202600
Sep 22, 20262148K
Sep 23, 2026121.6K
Sep 24, 202600
Sep 25, 2026166.8K
Sep 26, 202600
Sep 27, 202600
Sep 28, 2026235.1M
Sep 29, 2026110.8M
Sep 30, 2026226.3M
Oct 1, 202632.1M
Oct 2, 20262454K
Oct 3, 202600
Oct 4, 202600
Oct 5, 202627.3M
Oct 6, 202600
Oct 7, 202600
Oct 8, 20261333K
Oct 9, 2026121
Oct 10, 202600
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Oct 14, 20261493K
Oct 15, 202600
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Oct 20, 202612.2M
Oct 21, 2026121.2K
Oct 22, 202600
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Oct 25, 202600
Oct 26, 202600
Oct 27, 2026121M
Oct 28, 2026153.3M
Oct 29, 202614.2M
Oct 30, 20261269K
Oct 31, 202600
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Token mix

How the 1.7T tokens split. Cache reads are usually most of an agent's tokens and are priced far below input.

  • Input6.7%
  • Output0.4%
  • Cache read93%
  • Cache write<0.1%
  • Reasoning0.2%

Agents that carry GPT-5.3 Codex

Each agent's share of this model's tokens, and how many accounts used the model through it.

  1. Codex CLI85% of this model's tokens1,058active users
  2. OpenCode11% of this model's tokens514active users
  3. OpenClaw1.5% of this model's tokens86active users
  4. Pi1.2% of this model's tokens62active users
  5. Cursor0.7% of this model's tokens80active users
  6. Claude Code0.2% of this model's tokens31active users
  7. Hermes Agent<0.1% of this model's tokens44active users
  8. Oh My Pi<0.1% of this model's tokens10active users
  9. Roo Code<0.1% of this model's tokens2active users
  10. Amp<0.1% of this model's tokens3active users
  11. Copilot<0.1% of this model's tokens73active users
  12. Senpi (OmO Native)<0.1% of this model's tokens1active users
  13. Kilo<0.1% of this model's tokens7active users
  14. Junie<0.1% of this model's tokens1active users
  15. Zed Agent<0.1% of this model's tokens7active users
  16. MiMo Code<0.1% of this model's tokens1active users
  17. Cline<0.1% of this model's tokens2active users
  18. Goose<0.1% of this model's tokens3active users
  19. CodeBuddy<0.1% of this model's tokens3active users

Benchmarks

The best result for GPT-5.3 Codex in each benchmark Tokscale tracks. Each benchmark has its own unit, run conditions and source, so compare a score only with other models on the same benchmark. Rank is among the models Tokscale matched in that benchmark. Agent rows also show Tokscale usage through that agent, side by side with the score. Usage does not explain the score.

All benchmarks
  • WebDev ArenaWeb development · LMArena
    1,407
    Rank #76 of 110 matched modelsCodexPublished Oct 19, 2026Source: huggingface.co (opens in a new tab)

    Tokscale usage: Codex CLI carried 85% of GPT-5.3 Codex tokens across all time (1,058 active users).

    Show 1 more result with other run conditionsHide the other results
    • Listed as GPT-5.3-Codex (Codex Harness)
      1,371
      CodexPublished Oct 19, 2026Source: huggingface.co (opens in a new tab)
  • SWE-bench VerifiedHistory · Epoch AI
    74.8%
    Rank #15 of 32 matched modelsHighRun date Feb 25, 2026Source: epoch.ai (opens in a new tab)

    History only. Newer agentic coding benchmarks are on the main board.

Not evaluated: Epoch Capabilities Index, GPQA Diamond, Humanity's Last Exam, FrontierMath Tiers 1–3 (v2), SimpleQA Verified, Terminal-Bench 4.1, CursorBench, DeepSWE, FrontierSWE, Agent Arena. A missing result is not a score of zero.

Pricing

List prices from models.dev in US dollars per 1M tokens, retrieved Nov 12, 2026. OpenAI is the lab's own API and the reference price for the estimates below.

Price at real agent usageEstimate at list prices

What 1M tokens cost at the reference list price when they split into input, output and cache the way Tokscale users' agents used them. It is not what anyone paid.

At this model's mix
$0.36
Est. per 1M tokens
93% cache reads
At the community mix
$0.36
Est. per 1M tokens
93% cache reads
3:1 input/output blend
$4.81
Per 1M tokens at list prices
No cache

Cache reads were 93% of GPT-5.3 Codex tokens across all time and 93% of all tokens. The 3:1 blend, (3 × input + output) ÷ 4, is the usual comparison and ignores cache.

Assumptions

  • OpenAI list prices per 1M tokens: input $1.75, output $14, cache read $0.17.
  • This model's mix is its own input, output, cache and reasoning tokens across all time.
  • The community mix is the tokens of every identified model across all time.
  • This is an estimate at list prices, not what anyone paid.
  • Reasoning tokens are priced as output.
  • Long-context tiers, batch pricing and discounts are not applied.
  • Token mixes come from public Tokscale submissions. Hidden accounts are excluded.
  • No cache-write price is listed, so cache writes are priced as input.

List prices by provider

  • OpenAIReferencegpt-5.3-codex
    $1.75 input per 1M tokens$14 output per 1M tokens$0.17 cache read per 1M tokensCache write not listed400K tokens of context
  • OpenRouteropenai/gpt-5.3-codex
    $1.75 input per 1M tokens$14 output per 1M tokens$0.17 cache read per 1M tokensCache write not listed400K tokens of context
  • Vercel AI Gatewayopenai/gpt-5.3-codex
    $1.75 input per 1M tokens$14 output per 1M tokens$0.17 cache read per 1M tokensCache write not listed400K tokens of context

Specs

From models.dev (MIT), retrieved Nov 12, 2026, as the lab or the first listed provider reports them. A dash means models.dev does not list the value.

Context window
400K tokens
Max output
128K tokens
Input
Text, Image, PDF
Output
Text
Knowledge cutoff
Aug 31, 2025
Release date
Feb 5, 2026
Open weights
No
Reasoning
Yes
Tool calling
Yes

Top public profiles

Public profiles that used GPT-5.3 Codex most in this period, by tokens. Hidden accounts are never listed.

  1. 1aisubscriptionshare-debug@aisubscriptionshare-debug143.4Btokens$40.3Kest. cost61active days$40.3K est. cost · 61 active days
  2. 2AdEx-Partners-DE@AdEx-Partners-DE142.4Btokens$44.6Kest. cost56active days$44.6K est. cost · 56 active days
  3. 3Eugene@YuujinHwang90Btokens$25Kest. cost48active days$25K est. cost · 48 active days
  4. 4Daniil Sedov@Gusarich61.7Btokens$19.3Kest. cost32active days$19.3K est. cost · 32 active days
  5. 5weiyangzen@weiyangzen58.2Btokens$35.2Kest. cost66active days$35.2K est. cost · 66 active days
  6. 6Fabian@erophames55.8Btokens$16.7Kest. cost100active days$16.7K est. cost · 100 active days
  7. 7Buseong Kim@Skyline-2330.3Btokens$9.4Kest. cost40active days$9.4K est. cost · 40 active days
  8. 8Viktor Nagy@NagyVikt25.1Btokens$8Kest. cost68active days$8K est. cost · 68 active days
  9. 9laobeizi1996@laobeizi199624.4Btokens$8.1Kest. cost75active days$8.1K est. cost · 75 active days
  10. 10Chris Anderton@chrisanderton23.2Btokens$7.4Kest. cost111active days$7.4K est. cost · 111 active days

More from OpenAI

Other OpenAI models in the same period, by active users.

  1. GPT-5.565% adoption1,585active users
  2. GPT-5.462% adoption1,528active users
  3. GPT-5.6 Sol51% adoption1,260active users
  4. GPT-5.6 Terra45% adoption1,105active users
  5. GPT-5.6 Luna44% adoption1,078active users
  6. GPT-5.4 Mini42% adoption1,039active users
All models

Sources and attribution

Each score keeps its own source, unit and run conditions. Tokscale does not combine benchmarks into one score, and a missing score means the model was not evaluated, not that it scored zero.

  • Epoch AIEpoch Capabilities Index, Epoch's own runs and the external results it collects.CC BY 4.0 · Retrieved Nov 12, 2026 · Data through Nov 9, 2026Epoch AI, ‘Capabilities & benchmarking’. Published online at epoch.ai. Retrieved from https://epoch.ai/benchmarks.
  • LMArenaWebDev Arena and Agent Arena leaderboards.CC BY 4.0 · Retrieved Oct 21, 2026 · Data through Oct 19, 2026LMArena leaderboard dataset (lmarena-ai/leaderboard-dataset), Hugging Face.
  • models.devSpecs and list prices per provider.MIT · Retrieved Nov 12, 2026models.dev, an open catalog of AI models (github.com/anomalyco/models.dev).
  • TokscaleActive users, tokens and token mix from public Tokscale submissions. Hidden accounts are excluded.How we count