Pick Gemma 4 for self-hosted, data-private deployment or running locally or on edge devices. Pick GPT-5.3-Codex for dedicated coding agent or cli and ide integration. Choose Gemma 4 if you need self-hosting or data privacy; GPT-5.3-Codex if you want a managed API.
Gemma 4 (Google) and GPT-5.3-Codex (OpenAI) are two of the models people most often weigh against each other in 2026. Gemma 4 is google's open-weight family: Apache 2.0 licensed, multimodal, and sized from edge devices up, for private self-hosting. GPT-5.3-Codex is openAI's coding-specialized agent model for autonomous software engineering. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences
Cost model: Gemma 4 ships open weights you can self-host (hardware cost only, no per-token fee), while GPT-5.3-Codex is API-metered at $1.75/$14 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
Context window: GPT-5.3-Codex holds 1.6× more — 400K (~600 pages) vs 256K (~384 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Recency: Gemma 4 is the newer model by about 37 days (released April 2, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
Gemma 4
GPT-5.3-Codex
Provider
Google (US)
OpenAI (US)
Released
April 2, 2026
February 24, 2026
Context window
256K (~384 pages)
400K (~600 pages)
Price (in/out)
Open weight (self-host / free)
$1.75/$14 per 1M tokens
Open weight?
Yes — self-hostable
No — API only
Modalities
text, image, code
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Self-hosted, data-private deployment: Gemma 4 — A core design strength of Gemma 4.
Running locally or on edge devices: Gemma 4 — A core design strength of Gemma 4.
Fine-tuning on your own data: Gemma 4 — A core design strength of Gemma 4.
Dedicated coding agent: GPT-5.3-Codex — A core design strength of GPT-5.3-Codex.
CLI and IDE integration: GPT-5.3-Codex — A core design strength of GPT-5.3-Codex.
Autonomous software tasks: GPT-5.3-Codex — A core design strength of GPT-5.3-Codex.
Lowest cost at scale: Gemma 4 — At Open weight (self-host / free), it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: GPT-5.3-Codex — Its 400K window is about 1.6× larger, fitting roughly 600 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: Gemma 4 — At Open weight (self-host / free) it undercuts GPT-5.3-Codex, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: GPT-5.3-Codex — Larger 400K window fits more in one prompt.
A team with data-privacy or self-hosting needs: Gemma 4 — Open weights let you run it on your own hardware; GPT-5.3-Codex is API-only.
Anyone whose priority is self-hosted, data-private deployment: Gemma 4 — It is specifically built for that.
Anyone whose priority is dedicated coding agent: GPT-5.3-Codex — That is its strongest area.
Gemma 4: where it fits
Google's open-weight family: Apache 2.0 licensed, multimodal, and sized from edge devices up, for private self-hosting. Released April 2, 2026 by Google, it is built for self-hosted, data-private deployment, running locally or on edge devices, fine-tuning on your own data, and multimodal tasks over a 256K context.
Its trade-offs are real: trails frontier closed models on the hardest tasks, and needs your own hardware to run. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
GPT-5.3-Codex: where it fits
OpenAI's coding-specialized agent model for autonomous software engineering. Released February 24, 2026 by OpenAI, it is built for dedicated coding agent, cLI and IDE integration, autonomous software tasks, and tool calling.
Its trade-offs: coding-specialized, narrower general use, and retired in favor of GPT-5.5 Codex. At $1.75 in / $14 out per million tokens, it sits in the mid price band.
The bottom line for this matchup
The defining split here is open vs. closed. Gemma 4 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. GPT-5.3-Codex gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Frequently asked questions
Is Gemma 4 or GPT-5.3-Codex better for coding?
Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, Gemma 4 leans toward self-hosted, data-private deployment while GPT-5.3-Codex leans toward dedicated coding agent, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Gemma 4 or GPT-5.3-Codex?
Gemma 4 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while GPT-5.3-Codex is API-metered at $1.75/$14 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
GPT-5.3-Codex — 400K vs 256K, about 1.6× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Gemma 4 and GPT-5.3-Codex together?
Yes — a multi-model platform like LumiChats gives you Gemma 4, GPT-5.3-Codex and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, Gemma 4 or GPT-5.3-Codex?
Gemma 4 — released April 2, 2026, about 37 days after GPT-5.3-Codex.
Gemma 4 vs GPT-5.3-Codex
Google · US | OpenAI · US · Updated June 2026
Quick verdict
Pick Gemma 4 for self-hosted, data-private deployment or running locally or on edge devices. Pick GPT-5.3-Codex for dedicated coding agent or cli and ide integration. Choose Gemma 4 if you need self-hosting or data privacy; GPT-5.3-Codex if you want a managed API.
Gemma 4 (Google) and GPT-5.3-Codex (OpenAI) are two of the models people most often weigh against each other in 2026. Gemma 4 is google's open-weight family: Apache 2.0 licensed, multimodal, and sized from edge devices up, for private self-hosting. GPT-5.3-Codex is openAI's coding-specialized agent model for autonomous software engineering. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences at a glance
▸Cost model: Gemma 4 ships open weights you can self-host (hardware cost only, no per-token fee), while GPT-5.3-Codex is API-metered at $1.75/$14 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
▸Context window: GPT-5.3-Codex holds 1.6× more — 400K (~600 pages) vs 256K (~384 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: Gemma 4 is the newer model by about 37 days (released April 2, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
Gemma 4
GPT-5.3-Codex
Provider
Google (US)
OpenAI (US)
Released
April 2, 2026
February 24, 2026
Context window
256K (~384 pages)
400K (~600 pages)
Price (in/out)
Open weight (self-host / free)
$1.75/$14 per 1M tokens
Open weight?
Yes — self-hostable
No — API only
Modalities
text, image, code
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Self-hosted, data-private deployment
Gemma 4
A core design strength of Gemma 4.
Running locally or on edge devices
Gemma 4
A core design strength of Gemma 4.
Fine-tuning on your own data
Gemma 4
A core design strength of Gemma 4.
Dedicated coding agent
GPT-5.3-Codex
A core design strength of GPT-5.3-Codex.
CLI and IDE integration
GPT-5.3-Codex
A core design strength of GPT-5.3-Codex.
Autonomous software tasks
GPT-5.3-Codex
A core design strength of GPT-5.3-Codex.
Lowest cost at scale
Gemma 4
At Open weight (self-host / free), it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
GPT-5.3-Codex
Its 400K window is about 1.6× larger, fitting roughly 600 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Gemma 4
At Open weight (self-host / free) it undercuts GPT-5.3-Codex, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ GPT-5.3-Codex
Larger 400K window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ Gemma 4
Open weights let you run it on your own hardware; GPT-5.3-Codex is API-only.
Anyone whose priority is self-hosted, data-private deployment
→ Gemma 4
It is specifically built for that.
Anyone whose priority is dedicated coding agent
→ GPT-5.3-Codex
That is its strongest area.
Gemma 4: where it fits
Google's open-weight family: Apache 2.0 licensed, multimodal, and sized from edge devices up, for private self-hosting. Released April 2, 2026 by Google, it is built for self-hosted, data-private deployment, running locally or on edge devices, fine-tuning on your own data, and multimodal tasks over a 256K context.
Its trade-offs are real: trails frontier closed models on the hardest tasks, and needs your own hardware to run. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
GPT-5.3-Codex: where it fits
OpenAI's coding-specialized agent model for autonomous software engineering. Released February 24, 2026 by OpenAI, it is built for dedicated coding agent, cLI and IDE integration, autonomous software tasks, and tool calling.
Its trade-offs: coding-specialized, narrower general use, and retired in favor of GPT-5.5 Codex. At $1.75 in / $14 out per million tokens, it sits in the mid price band.
The bottom line for this matchup
The defining split here is open vs. closed. Gemma 4 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. GPT-5.3-Codex gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Want both Gemma 4 and GPT-5.3-Codex without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.
Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, Gemma 4 leans toward self-hosted, data-private deployment while GPT-5.3-Codex leans toward dedicated coding agent, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Gemma 4 or GPT-5.3-Codex?
Gemma 4 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while GPT-5.3-Codex is API-metered at $1.75/$14 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
GPT-5.3-Codex — 400K vs 256K, about 1.6× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Gemma 4 and GPT-5.3-Codex together?
Yes — a multi-model platform like LumiChats gives you Gemma 4, GPT-5.3-Codex and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, Gemma 4 or GPT-5.3-Codex?
Gemma 4 — released April 2, 2026, about 37 days after GPT-5.3-Codex.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.