Pick Command A for enterprise rag and retrieval or strong long-context retrieval accuracy. Pick GPT-5.2 for strong all-round reasoning or reliable structured output. On a tight budget at scale, GPT-5.2 is the value pick.
Command A (Cohere) and GPT-5.2 (OpenAI) are two of the models people most often weigh against each other in 2026. Command A is cohere's enterprise-focused model built for retrieval-augmented and grounded workloads. GPT-5.2 is a capable GPT-5-generation all-rounder, now succeeded by GPT-5.5. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Price: GPT-5.2 is about 1.7× cheaper on input ($1.5/$10 per 1M tokens vs $2.5/$10 per 1M tokens) — modest, but it adds up at steady volume.
Context window: GPT-5.2 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: GPT-5.2 is the newer model by about 11 months (released 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
Command A
GPT-5.2
Provider
Cohere (Global)
OpenAI (US)
Released
2025
2026
Context window
256K (~384 pages)
400K (~600 pages)
Price (in/out)
$2.5/$10 per 1M tokens
$1.5/$10 per 1M tokens
Open weight?
No — API only
No — API only
Modalities
text, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Enterprise RAG and retrieval: Command A — A core design strength of Command A.
Strong long-context retrieval accuracy: Command A — A core design strength of Command A.
Multilingual: Command A — A core design strength of Command A.
Strong all-round reasoning: GPT-5.2 — A core design strength of GPT-5.2.
Reliable structured output: GPT-5.2 — A core design strength of GPT-5.2.
Broad ecosystem and tooling: GPT-5.2 — A core design strength of GPT-5.2.
Lowest cost at scale: GPT-5.2 — At $1.5/$10 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: GPT-5.2 — 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: GPT-5.2 — At $1.5/$10 per 1M tokens it undercuts Command A, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: GPT-5.2 — Larger 400K window fits more in one prompt.
Anyone whose priority is enterprise rag and retrieval: Command A — It is specifically built for that.
Anyone whose priority is strong all-round reasoning: GPT-5.2 — That is its strongest area.
Command A: where it fits
Cohere's enterprise-focused model built for retrieval-augmented and grounded workloads. Released 2025 by Cohere, it is built for enterprise RAG and retrieval, strong long-context retrieval accuracy, multilingual, and tool use.
Its trade-offs are real: less consumer presence, and narrower modality support. At $2.5 in / $10 out per million tokens, it sits in the mid price band.
GPT-5.2: where it fits
A capable GPT-5-generation all-rounder, now succeeded by GPT-5.5. Released 2026 by OpenAI, it is built for strong all-round reasoning, reliable structured output, broad ecosystem and tooling, and professional workflows.
Its trade-offs: superseded by GPT-5.5, and smaller context than flagships. At $1.5 in / $10 out per million tokens, it sits in the mid price band.
The bottom line for this matchup
Command A and GPT-5.2 overlap enough that the right pick depends on your specific job. GPT-5.2 costs less per token; GPT-5.2 holds the larger context; and each leads in its own area — Command A for enterprise rag and retrieval, GPT-5.2 for strong all-round reasoning. Rather than crowning one, run the same hard task through both once and let the results decide.
Frequently asked questions
Is Command A or GPT-5.2 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, Command A leans toward enterprise rag and retrieval while GPT-5.2 leans toward strong all-round reasoning, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Command A or GPT-5.2?
GPT-5.2 is cheaper — $2.5/$10 per 1M tokens vs $1.5/$10 per 1M tokens, roughly 1.7× apart on input.
Which has the bigger context window?
GPT-5.2 — 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 Command A and GPT-5.2 together?
Yes — a multi-model platform like LumiChats gives you Command A, GPT-5.2 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, Command A or GPT-5.2?
GPT-5.2 — released 2026, about 11 months after Command A.
Command A vs GPT-5.2
Cohere · Global | OpenAI · US · Updated June 2026
Quick verdict
Pick Command A for enterprise rag and retrieval or strong long-context retrieval accuracy. Pick GPT-5.2 for strong all-round reasoning or reliable structured output. On a tight budget at scale, GPT-5.2 is the value pick.
Command A (Cohere) and GPT-5.2 (OpenAI) are two of the models people most often weigh against each other in 2026. Command A is cohere's enterprise-focused model built for retrieval-augmented and grounded workloads. GPT-5.2 is a capable GPT-5-generation all-rounder, now succeeded by GPT-5.5. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Price: GPT-5.2 is about 1.7× cheaper on input ($1.5/$10 per 1M tokens vs $2.5/$10 per 1M tokens) — modest, but it adds up at steady volume.
▸Context window: GPT-5.2 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: GPT-5.2 is the newer model by about 11 months (released 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
Command A
GPT-5.2
Provider
Cohere (Global)
OpenAI (US)
Released
2025
2026
Context window
256K (~384 pages)
400K (~600 pages)
Price (in/out)
$2.5/$10 per 1M tokens
$1.5/$10 per 1M tokens
Open weight?
No — API only
No — API only
Modalities
text, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Enterprise RAG and retrieval
Command A
A core design strength of Command A.
Strong long-context retrieval accuracy
Command A
A core design strength of Command A.
Multilingual
Command A
A core design strength of Command A.
Strong all-round reasoning
GPT-5.2
A core design strength of GPT-5.2.
Reliable structured output
GPT-5.2
A core design strength of GPT-5.2.
Broad ecosystem and tooling
GPT-5.2
A core design strength of GPT-5.2.
Lowest cost at scale
GPT-5.2
At $1.5/$10 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
GPT-5.2
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
→ GPT-5.2
At $1.5/$10 per 1M tokens it undercuts Command A, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ GPT-5.2
Larger 400K window fits more in one prompt.
Anyone whose priority is enterprise rag and retrieval
→ Command A
It is specifically built for that.
Anyone whose priority is strong all-round reasoning
→ GPT-5.2
That is its strongest area.
Command A: where it fits
Cohere's enterprise-focused model built for retrieval-augmented and grounded workloads. Released 2025 by Cohere, it is built for enterprise RAG and retrieval, strong long-context retrieval accuracy, multilingual, and tool use.
Its trade-offs are real: less consumer presence, and narrower modality support. At $2.5 in / $10 out per million tokens, it sits in the mid price band.
GPT-5.2: where it fits
A capable GPT-5-generation all-rounder, now succeeded by GPT-5.5. Released 2026 by OpenAI, it is built for strong all-round reasoning, reliable structured output, broad ecosystem and tooling, and professional workflows.
Its trade-offs: superseded by GPT-5.5, and smaller context than flagships. At $1.5 in / $10 out per million tokens, it sits in the mid price band.
The bottom line for this matchup
Command A and GPT-5.2 overlap enough that the right pick depends on your specific job. GPT-5.2 costs less per token; GPT-5.2 holds the larger context; and each leads in its own area — Command A for enterprise rag and retrieval, GPT-5.2 for strong all-round reasoning. Rather than crowning one, run the same hard task through both once and let the results decide.
Want both Command A and GPT-5.2 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, Command A leans toward enterprise rag and retrieval while GPT-5.2 leans toward strong all-round reasoning, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Command A or GPT-5.2?
GPT-5.2 is cheaper — $2.5/$10 per 1M tokens vs $1.5/$10 per 1M tokens, roughly 1.7× apart on input.
Which has the bigger context window?
GPT-5.2 — 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 Command A and GPT-5.2 together?
Yes — a multi-model platform like LumiChats gives you Command A, GPT-5.2 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, Command A or GPT-5.2?
GPT-5.2 — released 2026, about 11 months after Command A.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.