Gemini 4 Argon vs GPT-5 Chat vs Qwen MT Plus
Gemini 4 Argon comes out ahead, 52 to 31 and 11 on our weighted score, though GPT-5 Chat is 14% cheaper per token.
- Our pick
GoogleGemini 4 Argon
52/100- ECI166.1
- Price$2.00 / $10.00
- Context1.05M
OpenAIGPT-5 Chat
31/100- ECI—
- Price$1.25 / $10.00
- Context400K
Alibaba (Qwen)Qwen MT Plus
11/100- ECI—
- Price$2.46 / $7.37
- Context16K
Gemini 4 Argon is our pick
Gemini 4 Argon is the better all-round choice, scoring 52/100 against GPT-5 Chat (31) and Qwen MT Plus (11). It leads on inputs & features and context window. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceGPT-5 ChatGPT-5 Chat $3.44 · Qwen MT Plus $3.69 · Gemini 4 Argon $4.00 per 1M tokens (3:1 blend)
- Longest contextGemini 4 ArgonGemini 4 Argon 1,048,576 · GPT-5 Chat 400,000 · Qwen MT Plus 16,384 tokens
- Widest inputsGemini 4 ArgonGemini 4 Argon: Text, Images, PDFs, Audio, Video · GPT-5 Chat: Text, Images · Qwen MT Plus: Text
- Self-hostingNo open weightsAll three are available only through APIs
| Measure | Weight | Gemini 4 Argon | GPT-5 Chat | Qwen MT Plus |
|---|---|---|---|---|
| Price | 50% | 20 | 23 | 22 |
| Inputs & features | 30% | 100 | 35 | 0 |
| Context window | 20% | 61 | 44 | 0 |
| Overall | 100% | 52/100 | 31/100 | 11/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Gemini 4 ArgonGoogle | GPT-5 ChatOpenAI | Qwen MT PlusAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 166.1 | — | — |
| ECI rank | #7 of 165 | — | — |
| GPQA DiamondGraduate-level science questions | 96.4% | — | — |
| FrontierMath Tiers 1–3Research-level mathematics | 86.0% | — | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 100% | — | — |
| SimpleQA VerifiedShort factual questions | 76.0% | — | — |
| Price per million tokens | |||
| Input | $2.00 | $1.25 (best) | $2.46 |
| Output | $10.00 | $10.00 | $7.37 (best) |
| Cached input | $0.10 | — | — |
| Blended (3:1) | $4.00 | $3.44 (best) | $3.69 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Google API | Median of 5 providers | Official Alibaba API |
| Limits | |||
| Context window | 1,048,576 tokens (best) | 400,000 tokens | 16,384 tokens |
| Max output | 65,536 tokens | 128,000 tokens (best) | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | No |
| PDFs | Yes | No | No |
| Audio | Yes | No | No |
| Video | Yes | No | No |
| Reasoning | Yeslow · medium · high | Yes | No |
| Tool calling | Yes | No | No |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Proprietary | Proprietary | Proprietary |
| API model ID | gemini-4-argon | — | qwen-mt-plus |
| API providers | 24 (best) | 5 | 3 |
| Released | Oct 13, 2026 | Aug 7, 2025 | Jul 24, 2025 |
| Knowledge cutoff | Mar 2026 | Sep 30, 2024 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Gemini 4 Argon$40.00
- GPT-5 Chat$32.50
- Qwen MT Plus$39.34
Which should you choose?
Which is better: Gemini 4 Argon, GPT-5 Chat or Qwen MT Plus?
Gemini 4 Argon is the better all-round choice, scoring 52/100 against GPT-5 Chat (31) and Qwen MT Plus (11). It leads on inputs & features and context window. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Gemini 4 Argon, GPT-5 Chat or Qwen MT Plus?
GPT-5 Chat is cheaper at $1.25 input / $10.00 output per million tokens (median across 5 API providers). Qwen MT Plus costs $2.46 input / $7.37 output per million tokens (official Alibaba API price); Gemini 4 Argon costs $2.00 input / $10.00 output per million tokens (official Google API price). At a typical mix of three input tokens to one output token, that is $3.44 per million tokens for GPT-5 Chat versus $3.69 for Qwen MT Plus (1.1× as much) and $4.00 for Gemini 4 Argon (1.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Gemini 4 Argon has an ECI of 166.1, GPT-5 Chat has not been scored yet and Qwen MT Plus has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Gemini 4 Argon, GPT-5 Chat and Qwen MT Plus yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that GPT-5 Chat and Qwen MT Plus does not support tool calling, which most coding agents need.
Which has the bigger context window?
Gemini 4 Argon has the largest context window at 1,048,576 tokens, against 400,000 for GPT-5 Chat and 16,384 for Qwen MT Plus. Maximum output per response: Gemini 4 Argon up to 65,536, GPT-5 Chat up to 128,000, Qwen MT Plus up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Gemini 4 Argon accepts text, images, PDFs, audio and video; GPT-5 Chat accepts text and images; Qwen MT Plus accepts text. Gemini 4 Argon handles the widest range of inputs.
Are any of these open source?
No. Gemini 4 Argon, GPT-5 Chat and Qwen MT Plus are proprietary and only available through APIs and apps.
Which is newer?
Gemini 4 Argon is the newest, released Oct 13, 2026. GPT-5 Chat came out Aug 7, 2025; Qwen MT Plus came out Jul 24, 2025. Knowledge cutoff: Gemini 4 Argon Mar 2026, GPT-5 Chat Sep 30, 2024.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Nov 2, 2026.
