---
title: "Is GPT-4 Turbo still undominated? · Undominated.ai"
canonical: https://undominated.ai/check/openai__gpt-4-turbo/
description: "GPT-4 Turbo: whether it is still undominated. If anything in this catalogue is both better and cheaper, it is named, as of Oct 6, 2026. Unrated stays unrated."
---

# Is GPT-4 Turbo still undominated? · Undominated.ai

> GPT-4 Turbo: whether it is still undominated. If anything in this catalogue is both better and cheaper, it is named, as of Oct 6, 2026. Unrated stays unrated.

# Is GPT-4 Turbo a good deal?

Whether anything in this catalogue beats GPT-4 Turbo on both quality and price, and what you give up if it does. A computation on the current catalogue, not an opinion.

12 undominated of 145 · Oct 6, 2026

 [1 · Choose & set workload](#check-input)[2 · Read verdict](#check-verdict)[3 · Inspect alternatives](#check-options)

As of Oct 6, 2026, GPT-4 Turbo is dominated for Balanced on LMArena. Claude Opus 5.5 scores 240.0 higher and costs 47% less, with a covering envelope. 12 of 145 rated, priced standard models are undominated.

[Inspect model evidence](/models/openai__gpt-4-turbo/) [Compare differences & requirements](/compare/?models=openai%2Fgpt-4-turbo%2Canthropic%2Fclaude-opus-5.5)

 Current model

GPT-4 Turbo

 Balanced

3 tokens in per 1 out

 Balanced Summarise Chat Code gen Agentic
 Monthly spend (USD) Used only to say how many months a named switching cost would take to earn back. Leave blank to skip. Switching cost (USD)

Claude Opus 5.5 is both better and cheaper than GPT-4 Turbo: 240.0 points higher on LMArena and 47% less per million tokens, $7.00 cheaper at this mix.

 [GPT-4 Turbo](https://undominated.ai/models/openai__gpt-4-turbo/)

LMArena Elo · higher is better

Scale starts at 1040 Elo

 **

1271.7

Effective $/M · Balanced · lower is better

 **

$15.00/M

 [Claude Opus 5.5](https://undominated.ai/models/anthropic__claude-opus-5.5/)

LMArena Elo · higher is better

Scale starts at 1040 Elo

 **

1511.7

Effective $/M · Balanced · lower is better

 **

$8.00/M

GPT-4 Turbo takes text, image, returns up to 4,096 tokens from a 128,000-token context, and is listed by 1 seller.

Compared against 145 rated, priced models on this lens: 80 models dominate it and give up nothing, 5 more dominate it but give something up. GPT-4 Turbo scores 1271.7 at $15.00 per million tokens for this mix.

## Envelope-safe replacements

Each row scores at least as high, costs no more, and covers this model’s context, output, modalities, tools, and reasoning. A cheaper narrower model is not listed here.

| Model | LMArena | Effective $/M | You save |
| --- | --- | --- | --- |
| [Claude Opus 5.5](/models/anthropic__claude-opus-5.5/) | 1511.7 +240.0 | $8.00/M | 47% |
| [Claude Opus 4.6](/models/anthropic__claude-opus-4.6/) | 1503.2 +231.5 | $10.00/M | 33% |
| [Claude Opus 5](/models/anthropic__claude-opus-5/) | 1502.1 +230.4 | $10.00/M | 33% |
| [Gemini 3.8 Flash](/models/google__gemini-3.8-flash/) | 1497.0 +225.3 | $3.00/M | 80% |
| [MiMo-V2.6-Pro](/models/xiaomi__mimo-v2.6-pro/) | 1491.0 +219.3 | $0.540/M | 96% |
| [Muse Spark 1.3](/models/meta__muse-spark-1.3/) | 1489.9 +218.2 | $2.00/M | 87% |
| [Claude Opus 4.7](/models/anthropic__claude-opus-4.7/) | 1489.9 +218.2 | $10.00/M | 33% |
| [Gemini 3.7 Flash](/models/google__gemini-3.7-flash/) | 1487.0 +215.3 | $3.00/M | 80% |
| [Muse Spark 1.2](/models/meta__muse-spark-1.2/) | 1483.2 +211.5 | $2.00/M | 87% |
| [Gemini 3.1 Pro Preview](/models/google__gemini-3.1-pro-preview/) | 1480.2 +208.5 | $4.50/M | 70% |
| [Gemini 3.6 Flash](/models/google__gemini-3.6-flash/) | 1479.5 +207.8 | $1.50/M | 90% |
| [Muse Spark 1.1](/models/meta__muse-spark-1.1/) | 1479.2 +207.5 | $2.00/M | 87% |
| [Gemini 3.5 Flash](/models/google__gemini-3.5-flash/) | 1477.7 +206.0 | $3.38/M | 78% |
| [Kimi K3](/models/moonshotai__kimi-k3/) | 1475.9 +204.2 | $3.76/M | 75% |
| [GLM 5.3 Flash](/models/z-ai__glm-5.3-flash/) | 1469.6 +197.9 | $0.238/M | 98% |
| [GPT-5.5](/models/openai__gpt-5.5/) | 1466.8 +195.1 | $11.25/M | 25% |
| [Claude Sonnet 5.5](/models/anthropic__claude-sonnet-5.5/) | 1466.6 +194.9 | $4.00/M | 73% |
| [DeepSeek V4.1 Flash](/models/deepseek__deepseek-v4.1-flash/) | 1462.5 +190.8 | $0.182/M | 99% |
| [Claude Opus 4.8](/models/anthropic__claude-opus-4.8/) | 1461.0 +189.3 | $10.00/M | 33% |
| [Gemini 2.5 Pro](/models/google__gemini-2.5-pro/) | 1457.8 +186.1 | $3.44/M | 77% |
| [Claude Sonnet 4.6](/models/anthropic__claude-sonnet-4.6/) | 1457.8 +186.1 | $6.00/M | 60% |
| [MiMo-V2.6-Flash](/models/xiaomi__mimo-v2.6-flash/) | 1456.4 +184.7 | $0.175/M | 99% |
| [GPT-5.6 Sol](/models/openai__gpt-5.6-sol/) | 1456.3 +184.6 | $8.00/M | 47% |
| [Kimi K2.6](/models/moonshotai__kimi-k2.6/) | 1455.5 +183.8 | $0.961/M | 94% |
| [Qwen3.7 Plus](/models/qwen__qwen3.7-plus/) | 1454.6 +182.9 | $0.560/M | 96% |
| [GPT-5.4](/models/openai__gpt-5.4/) | 1452.2 +180.5 | $5.63/M | 63% |
| [Claude Opus 4.5](/models/anthropic__claude-opus-4.5/) | 1451.0 +179.3 | $10.00/M | 33% |
| [Grok 4.5](/models/x-ai__grok-4.5/) | 1448.1 +176.4 | $3.00/M | 80% |
| [GPT-5.6 Terra](/models/openai__gpt-5.6-terra/) | 1446.3 +174.6 | $4.50/M | 70% |
| [GPT-6.1 Sol](/models/openai__gpt-6.1-sol/) | 1445.6 +173.9 | $4.00/M | 73% |
| [Kimi K2.5](/models/moonshotai__kimi-k2.5/) | 1445.2 +173.5 | $1.20/M | 92% |
| [Gemma 4 31B](/models/google__gemma-4-31b-it/) | 1443.4 +171.7 | $0.205/M | 99% |
| [Claude Sonnet 5](/models/anthropic__claude-sonnet-5/) | 1442.9 +171.2 | $4.00/M | 73% |
| [Inkling](/models/thinkingmachines__inkling/) | 1441.8 +170.1 | $1.73/M | 89% |
| [Qwen3.8 27B](/models/qwen__qwen3.8-27b/) | 1440.7 +169.0 | $0.562/M | 96% |
| [Claude Sonnet 4.5](/models/anthropic__claude-sonnet-4.5/) | 1438.6 +166.9 | $6.00/M | 60% |
| [Qwen3.5 397B A17B](/models/qwen__qwen3.5-397b-a17b/) | 1438.0 +166.3 | $0.878/M | 94% |
| [GLM 5V Turbo](/models/z-ai__glm-5v-turbo/) | 1437.2 +165.5 | $1.90/M | 87% |
| [Qwen3.6 Plus](/models/qwen__qwen3.6-plus/) | 1436.7 +165.0 | $0.731/M | 95% |
| [Gemini 3.5 Flash Lite](/models/google__gemini-3.5-flash-lite/) | 1434.7 +163.0 | $0.850/M | 94% |
| [Gemma 4 26B A4B](/models/google__gemma-4-26b-a4b-it/) | 1433.9 +162.2 | $0.127/M | 99% |
| [MiniMax M3](/models/minimax__minimax-m3/) | 1432.1 +160.4 | $0.485/M | 97% |
| [GPT-5.6 Luna](/models/openai__gpt-5.6-luna/) | 1431.0 +159.3 | $0.450/M | 97% |
| [MiMo-V2.5](/models/xiaomi__mimo-v2.5/) | 1427.5 +155.8 | $0.175/M | 99% |
| [Grok 4.6](/models/x-ai__grok-4.6/) | 1427.4 +155.7 | $3.00/M | 80% |
| [GPT-5.1](/models/openai__gpt-5.1/) | 1422.2 +150.5 | $3.44/M | 77% |
| [Mistral Medium 3.5](/models/mistralai__mistral-medium-3-5/) | 1421.2 +149.5 | $3.00/M | 80% |
| [Qwen3 VL 235B A22B Instruct](/models/qwen__qwen3-vl-235b-a22b-instruct/) | 1420.1 +148.4 | $0.370/M | 98% |
| [Qwen3.5-122B-A10B](/models/qwen__qwen3.5-122b-a10b/) | 1417.1 +145.4 | $0.715/M | 95% |
| [Gemini 2.5 Flash](/models/google__gemini-2.5-flash/) | 1417.0 +145.3 | $0.850/M | 94% |
| [Gemini 3.1 Flash Lite Preview](/models/google__gemini-3.1-flash-lite-preview/) | 1415.7 +144.0 | $0.563/M | 96% |
| [Inkling Small](/models/thinkingmachines__inkling-small/) | 1413.7 +142.0 | $0.638/M | 96% |
| [GPT-5.2](/models/openai__gpt-5.2/) | 1412.3 +140.6 | $4.81/M | 68% |
| [GPT-5.4 Mini](/models/openai__gpt-5.4-mini/) | 1411.4 +139.7 | $1.69/M | 89% |
| [o3](/models/openai__o3/) | 1409.9 +138.2 | $3.50/M | 77% |
| [Qwen3.5-27B](/models/qwen__qwen3.5-27b/) | 1408.9 +137.2 | $0.536/M | 96% |
| [GPT-5](/models/openai__gpt-5/) | 1406.1 +134.4 | $3.44/M | 77% |
| [Qwen3 VL 235B A22B Thinking](/models/qwen__qwen3-vl-235b-a22b-thinking/) | 1400.7 +129.0 | $1.30/M | 91% |
| [Grok 4.7](/models/x-ai__grok-4.7/) | 1399.9 +128.2 | $3.00/M | 80% |
| [Qwen3.5-Flash](/models/qwen__qwen3.5-flash-02-23/) | 1397.0 +125.3 | $0.114/M | 99% |
| [Grok 4.3](/models/x-ai__grok-4.3/) | 1396.7 +125.0 | $1.56/M | 90% |
| [Claude Haiku 4.5](/models/anthropic__claude-haiku-4.5/) | 1396.1 +124.4 | $2.00/M | 87% |
| [GPT-6 Sol](/models/openai__gpt-6-sol/) | 1395.4 +123.7 | $4.00/M | 73% |
| [Qwen3.5-35B-A3B](/models/qwen__qwen3.5-35b-a3b/) | 1394.4 +122.7 | $0.355/M | 98% |
| [GPT-6 Luna](/models/openai__gpt-6-luna/) | 1391.5 +119.8 | $0.200/M | 99% |
| [GPT-4.1](/models/openai__gpt-4.1/) | 1383.0 +111.3 | $3.50/M | 77% |
| [GLM 4.6V](/models/z-ai__glm-4.6v/) | 1376.8 +105.1 | $0.450/M | 97% |
| [GPT-5 Mini](/models/openai__gpt-5-mini/) | 1373.2 +101.5 | $0.688/M | 95% |
| [GPT-5.4 Nano](/models/openai__gpt-5.4-nano/) | 1372.1 +100.4 | $0.463/M | 97% |
| [Nova 2 Lite](/models/amazon__nova-2-lite-v1/) | 1361.8 +90.1 | $0.850/M | 94% |
| [Gemma 3 27B](/models/google__gemma-3-27b-it/) | 1357.8 +86.1 | $0.100/M | 99% |
| [o4 Mini](/models/openai__o4-mini/) | 1353.2 +81.5 | $1.93/M | 87% |
| [GPT-4.1 Mini](/models/openai__gpt-4.1-mini/) | 1340.4 +68.7 | $0.700/M | 95% |
| [Claude Sonnet 4](/models/anthropic__claude-sonnet-4/) | 1339.1 +67.4 | $6.00/M | 60% |
| [Gemma 3 12B](/models/google__gemma-3-12b-it/) | 1334.2 +62.5 | $0.075/M | 100% |
| [GPT-5 Nano](/models/openai__gpt-5-nano/) | 1320.0 +48.3 | $0.138/M | 99% |
| [GPT-4o (2024-05-13)](/models/openai__gpt-4o-2024-05-13/) | 1300.4 +28.7 | $7.50/M | 50% |
| [GPT-4o-mini (2024-07-18)](/models/openai__gpt-4o-mini-2024-07-18/) | 1286.4 +14.7 | $0.263/M | 98% |
| [GPT-4.1 Nano](/models/openai__gpt-4.1-nano/) | 1284.8 +13.1 | $0.175/M | 99% |
| [GPT-4o (2024-08-06)](/models/openai__gpt-4o-2024-08-06/) | 1282.5 +10.8 | $4.38/M | 71% |

## Higher score, lower price, named losses

Not a drop-in. The loss is why these are not a recommendation.

| Model | LMArena | Effective $/M | You give up |
| --- | --- | --- | --- |
| [GLM 5.3](/models/z-ai__glm-5.3/) | 1471.4 | $1.00/M | image |
| [GLM 5.2](/models/z-ai__glm-5.2/) | 1470.4 | $1.12/M | image |
| [MiMo-V2.5-Pro](/models/xiaomi__mimo-v2.5-pro/) | 1465.0 | $0.544/M | image |
| [GLM 5.1](/models/z-ai__glm-5.1/) | 1461.5 | $1.83/M | image |
| [Qwen3.6 Max Preview](/models/qwen__qwen3.6-max-preview/) | 1446.8 | $2.31/M | image |

 145 rated, priced standard models. Chartreuse is the frontier. Cobalt is the model you named, when it is not on the staircase.

Copy watch URL [Open the frontier](/frontier/)

The link keeps the model and mix, using the current catalogue. Monthly spend and switching cost stay in this browser tab and are left out of the link.

## Saved decision references

Save the model, workload, catalogue date and capability-preservation rule in this browser. Spend, switching cost and bill contents are not saved. No account or notifications.

Constraint: replacements must preserve the model’s capabilities; any losses remain named trade-offs.

Historical decisions cannot be fully replayed from saved references: past prices, scores and capability evidence are not stored.

 Save this reference

## Continue your investigation

 - [Build a shortlist](/compare/)
- [Inspect alternatives](/alternatives/)
- [Check billing conditions](/traps/)
- [Read model evidence](/models/)